未验证 提交 731b3b32 编写于 作者: K Kaipeng Deng 提交者: GitHub

update link for release/2.4. (#5489)

* update link for release/2.4. test=document_fix
上级 fa52c445
......@@ -4,12 +4,12 @@
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | Mask AP | 下载 | 配置文件 |
| :------------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----: | :-----------------------------------------------------: | :-----: |
| ResNet50-FPN | Cascade Faster | 1 | 1x | ---- | 41.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.yml) |
| ResNet50-FPN | Cascade Mask | 1 | 1x | ---- | 41.8 | 36.3 | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Faster | 1 | 1x | ---- | 44.4 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Faster | 1 | 2x | ---- | 45.0 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Mask | 1 | 1x | ---- | 44.9 | 39.1 | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_mask_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Mask | 1 | 2x | ---- | 45.7 | 39.7 | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
| ResNet50-FPN | Cascade Faster | 1 | 1x | ---- | 41.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.yml) |
| ResNet50-FPN | Cascade Mask | 1 | 1x | ---- | 41.8 | 36.3 | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Faster | 1 | 1x | ---- | 44.4 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/cascade_rcnn/cascade_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Faster | 1 | 2x | ---- | 45.0 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/cascade_rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Mask | 1 | 1x | ---- | 44.9 | 39.1 | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/cascade_rcnn/cascade_mask_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Mask | 1 | 2x | ---- | 45.7 | 39.7 | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/cascade_rcnn/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
## Citations
......
......@@ -2,17 +2,17 @@
| 骨架网络 | 网络类型 | 卷积 | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | Mask AP | 下载 | 配置文件 |
| :------------------- | :------------- | :-----: |:--------: | :-----: | :-----------: |:----: | :-----: | :----------------------------------------------------------: | :----: |
| ResNet50-FPN | Faster | c3-c5 | 1 | 1x | - | 42.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/faster_rcnn_dcn_r50_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Faster | c3-c5 | 1 | 1x | - | 42.7 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r50_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/faster_rcnn_dcn_r50_vd_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Faster | c3-c5 | 1 | 2x | - | 43.7 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r50_vd_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/faster_rcnn_dcn_r50_vd_fpn_2x_coco.yml) |
| ResNet101-vd-FPN | Faster | c3-c5 | 1 | 1x | - | 45.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r101_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/faster_rcnn_dcn_r101_vd_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Faster | c3-c5 | 1 | 1x | - | 46.5 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) |[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNet50-FPN | Mask | c3-c5 | 1 | 1x | - | 42.7 | 38.4 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/mask_rcnn_dcn_r50_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Mask | c3-c5 | 1 | 2x | - | 44.6 | 39.8 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r50_vd_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/mask_rcnn_dcn_r50_vd_fpn_2x_coco.yml) |
| ResNet101-vd-FPN | Mask | c3-c5 | 1 | 1x | - | 45.6 | 40.6 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r101_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/mask_rcnn_dcn_r101_vd_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Mask | c3-c5 | 1 | 1x | - | 47.3 | 42.0 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNet50-FPN | Cascade Faster | c3-c5 | 1 | 1x | - | 42.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_dcn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/cascade_rcnn_dcn_r50_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Cascade Faster | c3-c5 | 1 | 1x | - | 48.8 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNet50-FPN | Faster | c3-c5 | 1 | 1x | - | 42.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dcn/faster_rcnn_dcn_r50_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Faster | c3-c5 | 1 | 1x | - | 42.7 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r50_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dcn/faster_rcnn_dcn_r50_vd_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Faster | c3-c5 | 1 | 2x | - | 43.7 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r50_vd_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dcn/faster_rcnn_dcn_r50_vd_fpn_2x_coco.yml) |
| ResNet101-vd-FPN | Faster | c3-c5 | 1 | 1x | - | 45.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r101_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dcn/faster_rcnn_dcn_r101_vd_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Faster | c3-c5 | 1 | 1x | - | 46.5 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) |[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dcn/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNet50-FPN | Mask | c3-c5 | 1 | 1x | - | 42.7 | 38.4 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dcn/mask_rcnn_dcn_r50_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Mask | c3-c5 | 1 | 2x | - | 44.6 | 39.8 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r50_vd_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dcn/mask_rcnn_dcn_r50_vd_fpn_2x_coco.yml) |
| ResNet101-vd-FPN | Mask | c3-c5 | 1 | 1x | - | 45.6 | 40.6 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r101_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dcn/mask_rcnn_dcn_r101_vd_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Mask | c3-c5 | 1 | 1x | - | 47.3 | 42.0 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dcn/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNet50-FPN | Cascade Faster | c3-c5 | 1 | 1x | - | 42.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_dcn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dcn/cascade_rcnn_dcn_r50_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Cascade Faster | c3-c5 | 1 | 1x | - | 48.8 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dcn/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
**注意事项:**
......
......@@ -10,7 +10,7 @@ Deformable DETR is an object detection model based on DETR. We reproduced the mo
| Backbone | Model | Images/GPU | Inf time (fps) | Box AP | Config | Download |
|:------:|:--------:|:--------:|:--------------:|:------:|:------:|:--------:|
| R-50 | Deformable DETR | 2 | --- | 44.1 | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/deformable_detr/deformable_detr_r50_1x_coco.yml) | [model](https://paddledet.bj.bcebos.com/models/deformable_detr_r50_1x_coco.pdparams) |
| R-50 | Deformable DETR | 2 | --- | 44.1 | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/deformable_detr/deformable_detr_r50_1x_coco.yml) | [model](https://paddledet.bj.bcebos.com/models/deformable_detr_r50_1x_coco.pdparams) |
**Notes:**
......
......@@ -10,7 +10,7 @@ DETR is an object detection model based on transformer. We reproduced the model
| Backbone | Model | Images/GPU | Inf time (fps) | Box AP | Config | Download |
|:------:|:--------:|:--------:|:--------------:|:------:|:------:|:--------:|
| R-50 | DETR | 4 | --- | 42.3 | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/detr/detr_r50_1x_coco.yml) | [model](https://paddledet.bj.bcebos.com/models/detr_r50_1x_coco.pdparams) |
| R-50 | DETR | 4 | --- | 42.3 | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/detr/detr_r50_1x_coco.yml) | [model](https://paddledet.bj.bcebos.com/models/detr_r50_1x_coco.pdparams) |
**Notes:**
......
......@@ -142,8 +142,8 @@ python3.7 tools/infer.py -c configs/dota/s2anet_alignconv_2x_dota.yml -o weights
| 模型 | Conv类型 | mAP | 模型下载 | 配置文件 |
|:-----------:|:----------:|:--------:| :----------:| :---------: |
| S2ANet | Conv | 71.42 | [model](https://paddledet.bj.bcebos.com/models/s2anet_conv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dota/s2anet_conv_2x_dota.yml) |
| S2ANet | AlignConv | 74.0 | [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dota/s2anet_alignconv_2x_dota.yml) |
| S2ANet | Conv | 71.42 | [model](https://paddledet.bj.bcebos.com/models/s2anet_conv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dota/s2anet_conv_2x_dota.yml) |
| S2ANet | AlignConv | 74.0 | [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dota/s2anet_alignconv_2x_dota.yml) |
**注意:** 这里使用`multiclass_nms`,与原作者使用nms略有不同。
......
......@@ -152,8 +152,8 @@ Please refer to [DOTA_devkit](https://github.com/CAPTAIN-WHU/DOTA_devkit) genera
| Model | Conv Type | mAP | Model Download | Configuration File |
|:-----------:|:----------:|:--------:| :----------:| :---------: |
| S2ANet | Conv | 71.42 | [model](https://paddledet.bj.bcebos.com/models/s2anet_conv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dota/s2anet_conv_2x_dota.yml) |
| S2ANet | AlignConv | 74.0 | [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dota/s2anet_alignconv_2x_dota.yml) |
| S2ANet | Conv | 71.42 | [model](https://paddledet.bj.bcebos.com/models/s2anet_conv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dota/s2anet_conv_2x_dota.yml) |
| S2ANet | AlignConv | 74.0 | [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/dota/s2anet_alignconv_2x_dota.yml) |
**Attention:** `multiclass_nms` is used here, which is slightly different from the original author's use of NMS.
......
......@@ -11,8 +11,8 @@
| 网络结构 | 输入尺寸 | 图片个数/GPU | 学习率策略 | Easy/Medium/Hard Set | 预测时延(SD855)| 模型大小(MB) | 下载 | 配置文件 |
|:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:|
| BlazeFace | 640 | 8 | 1000e | 0.885 / 0.855 / 0.731 | - | 0.472 |[下载链接](https://paddledet.bj.bcebos.com/models/blazeface_1000e.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/face_detection/blazeface_1000e.yml) |
| BlazeFace-FPN-SSH | 640 | 8 | 1000e | 0.907 / 0.883 / 0.793 | - | 0.479 |[下载链接](https://paddledet.bj.bcebos.com/models/blazeface_fpn_ssh_1000e.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/face_detection/blazeface_fpn_ssh_1000e.yml) |
| BlazeFace | 640 | 8 | 1000e | 0.885 / 0.855 / 0.731 | - | 0.472 |[下载链接](https://paddledet.bj.bcebos.com/models/blazeface_1000e.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/face_detection/blazeface_1000e.yml) |
| BlazeFace-FPN-SSH | 640 | 8 | 1000e | 0.907 / 0.883 / 0.793 | - | 0.479 |[下载链接](https://paddledet.bj.bcebos.com/models/blazeface_fpn_ssh_1000e.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/face_detection/blazeface_fpn_ssh_1000e.yml) |
**注意:**
- 我们使用多尺度评估策略得到`Easy/Medium/Hard Set`里的mAP。具体细节请参考[在WIDER-FACE数据集上评估](#在WIDER-FACE数据集上评估)
......
......@@ -11,8 +11,8 @@
| Network structure | size | images/GPUs | Learning rate strategy | Easy/Medium/Hard Set | Prediction delay(SD855)| Model size(MB) | Download | Configuration File |
|:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:|
| BlazeFace | 640 | 8 | 1000e | 0.885 / 0.855 / 0.731 | - | 0.472 |[link](https://paddledet.bj.bcebos.com/models/blazeface_1000e.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/face_detection/blazeface_1000e.yml) |
| BlazeFace-FPN-SSH | 640 | 8 | 1000e | 0.907 / 0.883 / 0.793 | - | 0.479 |[link](https://paddledet.bj.bcebos.com/models/blazeface_fpn_ssh_1000e.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/face_detection/blazeface_fpn_ssh_1000e.yml) |
| BlazeFace | 640 | 8 | 1000e | 0.885 / 0.855 / 0.731 | - | 0.472 |[link](https://paddledet.bj.bcebos.com/models/blazeface_1000e.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/face_detection/blazeface_1000e.yml) |
| BlazeFace-FPN-SSH | 640 | 8 | 1000e | 0.907 / 0.883 / 0.793 | - | 0.479 |[link](https://paddledet.bj.bcebos.com/models/blazeface_fpn_ssh_1000e.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/face_detection/blazeface_fpn_ssh_1000e.yml) |
**Attention:**
- We use a multi-scale evaluation strategy to get the mAP in `Easy/Medium/Hard Set`. Please refer to the [evaluation on the WIDER FACE dataset](#Evaluated-on-the-WIDER-FACE-Dataset) for details.
......
......@@ -12,9 +12,9 @@ FCOS (Fully Convolutional One-Stage Object Detection) is a fast anchor-free obje
| Backbone | Model | images/GPU | lr schedule |FPS | Box AP | download | config |
| :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
| ResNet50-FPN | FCOS | 2 | 1x | ---- | 39.6 | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/fcos/fcos_r50_fpn_1x_coco.yml) |
| ResNet50-FPN | FCOS+DCN | 2 | 1x | ---- | 44.3 | [download](https://paddledet.bj.bcebos.com/models/fcos_dcn_r50_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/fcos/fcos_dcn_r50_fpn_1x_coco.yml) |
| ResNet50-FPN | FCOS+multiscale_train | 2 | 2x | ---- | 41.8 | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_multiscale_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/fcos/fcos_r50_fpn_multiscale_2x_coco.yml) |
| ResNet50-FPN | FCOS | 2 | 1x | ---- | 39.6 | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/fcos/fcos_r50_fpn_1x_coco.yml) |
| ResNet50-FPN | FCOS+DCN | 2 | 1x | ---- | 44.3 | [download](https://paddledet.bj.bcebos.com/models/fcos_dcn_r50_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/fcos/fcos_dcn_r50_fpn_1x_coco.yml) |
| ResNet50-FPN | FCOS+multiscale_train | 2 | 2x | ---- | 41.8 | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_multiscale_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/fcos/fcos_r50_fpn_multiscale_2x_coco.yml) |
**Notes:**
......
......@@ -8,11 +8,11 @@ We reproduce the object detection results in the paper [Generalized Focal Loss:
| Backbone | Model | batch-size/GPU | lr schedule |FPS | Box AP | download | config |
| :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
| ResNet50 | GFL | 2 | 1x | ---- | 41.0 | [model](https://paddledet.bj.bcebos.com/models/gfl_r50_fpn_1x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gfl_r50_fpn_1x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gfl/gfl_r50_fpn_1x_coco.yml) |
| ResNet101-vd | GFL | 2 | 2x | ---- | 46.8 | [model](https://paddledet.bj.bcebos.com/models/gfl_r101vd_fpn_mstrain_2x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gfl_r101vd_fpn_mstrain_2x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gfl/gfl_r101vd_fpn_mstrain_2x_coco.yml) |
| ResNet34-vd | GFL | 2 | 1x | ---- | 40.8 | [model](https://paddledet.bj.bcebos.com/models/gfl_r34vd_1x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gfl_r34vd_1x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gfl/gfl_r34vd_1x_coco.yml) |
| ResNet18-vd | GFL | 2 | 1x | ---- | 36.6 | [model](https://paddledet.bj.bcebos.com/models/gfl_r18vd_1x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gfl_r18vd_1x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gfl/gfl_r18vd_1x_coco.yml) |
| ResNet50 | GFLv2 | 2 | 1x | ---- | 41.2 | [model](https://paddledet.bj.bcebos.com/models/gflv2_r50_fpn_1x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gflv2_r50_fpn_1x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gfl/gflv2_r50_fpn_1x_coco.yml) |
| ResNet50 | GFL | 2 | 1x | ---- | 41.0 | [model](https://paddledet.bj.bcebos.com/models/gfl_r50_fpn_1x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gfl_r50_fpn_1x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/gfl/gfl_r50_fpn_1x_coco.yml) |
| ResNet101-vd | GFL | 2 | 2x | ---- | 46.8 | [model](https://paddledet.bj.bcebos.com/models/gfl_r101vd_fpn_mstrain_2x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gfl_r101vd_fpn_mstrain_2x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/gfl/gfl_r101vd_fpn_mstrain_2x_coco.yml) |
| ResNet34-vd | GFL | 2 | 1x | ---- | 40.8 | [model](https://paddledet.bj.bcebos.com/models/gfl_r34vd_1x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gfl_r34vd_1x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/gfl/gfl_r34vd_1x_coco.yml) |
| ResNet18-vd | GFL | 2 | 1x | ---- | 36.6 | [model](https://paddledet.bj.bcebos.com/models/gfl_r18vd_1x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gfl_r18vd_1x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/gfl/gfl_r18vd_1x_coco.yml) |
| ResNet50 | GFLv2 | 2 | 1x | ---- | 41.2 | [model](https://paddledet.bj.bcebos.com/models/gflv2_r50_fpn_1x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gflv2_r50_fpn_1x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/gfl/gflv2_r50_fpn_1x_coco.yml) |
**Notes:**
......
......@@ -4,10 +4,10 @@
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | Mask AP | 下载 | 配置文件 |
| :------------- | :------------- | :-----------: | :------: | :--------: |:-----: | :-----: | :----: | :----: |
| ResNet50-FPN | Faster | 1 | 2x | - | 41.9 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gn/faster_rcnn_r50_fpn_gn_2x_coco.yml) |
| ResNet50-FPN | Mask | 1 | 2x | - | 42.3 | 38.4 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gn/mask_rcnn_r50_fpn_gn_2x_coco.yml) |
| ResNet50-FPN | Cascade Faster | 1 | 2x | - | 44.6 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gn/cascade_rcnn_r50_fpn_gn_2x_coco.yml) |
| ResNet50-FPN | Cacade Mask | 1 | 2x | - | 45.0 | 39.3 | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gn/cascade_mask_rcnn_r50_fpn_gn_2x_coco.yml) |
| ResNet50-FPN | Faster | 1 | 2x | - | 41.9 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/gn/faster_rcnn_r50_fpn_gn_2x_coco.yml) |
| ResNet50-FPN | Mask | 1 | 2x | - | 42.3 | 38.4 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/gn/mask_rcnn_r50_fpn_gn_2x_coco.yml) |
| ResNet50-FPN | Cascade Faster | 1 | 2x | - | 44.6 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/gn/cascade_rcnn_r50_fpn_gn_2x_coco.yml) |
| ResNet50-FPN | Cacade Mask | 1 | 2x | - | 45.0 | 39.3 | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/gn/cascade_mask_rcnn_r50_fpn_gn_2x_coco.yml) |
**注意:** Faster R-CNN baseline仅使用 `2fc` head,而此处使用[`4conv1fc` head](https://arxiv.org/abs/1803.08494)(4层conv之间使用GN),并且FPN也使用GN,而对于Mask R-CNN是在mask head的4层conv之间也使用GN。
......
......@@ -30,5 +30,5 @@
| Backbone | Type | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | Configs |
| :---------------------- | :------------- | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
| HRNetV2p_W18 | Faster | 1 | 1x | - | 36.8 | - | [model](https://paddledet.bj.bcebos.com/models/faster_rcnn_hrnetv2p_w18_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.yml) |
| HRNetV2p_W18 | Faster | 1 | 2x | - | 39.0 | - | [model](https://paddledet.bj.bcebos.com/models/faster_rcnn_hrnetv2p_w18_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.yml) |
| HRNetV2p_W18 | Faster | 1 | 1x | - | 36.8 | - | [model](https://paddledet.bj.bcebos.com/models/faster_rcnn_hrnetv2p_w18_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.yml) |
| HRNetV2p_W18 | Faster | 1 | 2x | - | 39.0 | - | [model](https://paddledet.bj.bcebos.com/models/faster_rcnn_hrnetv2p_w18_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.yml) |
......@@ -46,7 +46,7 @@ MPII数据集
### 1、环境安装
​ 请参考PaddleDetection [安装文档](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/INSTALL_cn.md)正确安装PaddlePaddle和PaddleDetection即可。
​ 请参考PaddleDetection [安装文档](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/INSTALL_cn.md)正确安装PaddlePaddle和PaddleDetection即可。
### 2、数据准备
......
......@@ -46,7 +46,7 @@ We also release [PP-TinyPose](./tiny_pose/README_en.md), a real-time keypoint de
### 1. Environmental Installation
​ Please refer to [PaddleDetection Installation Guild](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/INSTALL.md) to install PaddlePaddle and PaddleDetection correctly.
​ Please refer to [PaddleDetection Installation Guild](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/INSTALL.md) to install PaddlePaddle and PaddleDetection correctly.
### 2. Dataset Preparation
......
......@@ -4,18 +4,18 @@
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | Mask AP | 下载 | 配置文件 |
| :------------------- | :------------| :-----: | :-----: | :------------: | :-----: | :-----: | :-----------------------------------------------------: | :-----: |
| ResNet50 | Mask | 1 | 1x | ---- | 37.4 | 32.8 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_1x_coco.yml) |
| ResNet50 | Mask | 1 | 2x | ---- | 39.7 | 34.5 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_2x_coco.yml) |
| ResNet50-FPN | Mask | 1 | 1x | ---- | 39.2 | 35.6 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.yml) |
| ResNet50-FPN | Mask | 1 | 2x | ---- | 40.5 | 36.7 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.yml) |
| ResNet50-vd-FPN | Mask | 1 | 1x | ---- | 40.3 | 36.4 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Mask | 1 | 2x | ---- | 41.4 | 37.5 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_2x_coco.yml) |
| ResNet101-FPN | Mask | 1 | 1x | ---- | 40.6 | 36.6 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r101_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.yml) |
| ResNet101-vd-FPN | Mask | 1 | 1x | ---- | 42.4 | 38.1 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r101_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r101_vd_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Mask | 1 | 1x | ---- | 44.0 | 39.5 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_x101_vd_64x4d_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Mask | 1 | 2x | ---- | 44.6 | 39.8 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_x101_vd_64x4d_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_x101_vd_64x4d_fpn_2x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Mask | 1 | 1x | ---- | 42.0 | 38.2 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Mask | 1 | 2x | ---- | 42.7 | 38.9 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
| ResNet50 | Mask | 1 | 1x | ---- | 37.4 | 32.8 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r50_1x_coco.yml) |
| ResNet50 | Mask | 1 | 2x | ---- | 39.7 | 34.5 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r50_2x_coco.yml) |
| ResNet50-FPN | Mask | 1 | 1x | ---- | 39.2 | 35.6 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.yml) |
| ResNet50-FPN | Mask | 1 | 2x | ---- | 40.5 | 36.7 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.yml) |
| ResNet50-vd-FPN | Mask | 1 | 1x | ---- | 40.3 | 36.4 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Mask | 1 | 2x | ---- | 41.4 | 37.5 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_2x_coco.yml) |
| ResNet101-FPN | Mask | 1 | 1x | ---- | 40.6 | 36.6 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r101_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.yml) |
| ResNet101-vd-FPN | Mask | 1 | 1x | ---- | 42.4 | 38.1 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r101_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r101_vd_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Mask | 1 | 1x | ---- | 44.0 | 39.5 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_x101_vd_64x4d_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Mask | 1 | 2x | ---- | 44.6 | 39.8 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_x101_vd_64x4d_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_x101_vd_64x4d_fpn_2x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Mask | 1 | 1x | ---- | 42.0 | 38.2 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Mask | 1 | 2x | ---- | 42.7 | 38.9 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
## Citations
......
......@@ -31,19 +31,19 @@ PP-tracking provides an AI studio public project tutorial. Please refer to this
| backbone | input shape | MOTA | IDF1 | IDS | FP | FN | FPS | download | config |
| :----------------- | :------- | :----: | :----: | :---: | :----: | :---: | :---: | :---: | :---: |
| DarkNet53 | 1088x608 | 72.0 | 66.9 | 1397 | 7274 | 22209 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_1088x608.yml) |
| DarkNet53 | 864x480 | 69.1 | 64.7 | 1539 | 7544 | 25046 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_864x480.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_864x480.yml) |
| DarkNet53 | 576x320 | 63.7 | 64.4 | 1310 | 6782 | 31964 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_576x320.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_576x320.yml) |
| DarkNet53 | 1088x608 | 72.0 | 66.9 | 1397 | 7274 | 22209 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mot/jde/jde_darknet53_30e_1088x608.yml) |
| DarkNet53 | 864x480 | 69.1 | 64.7 | 1539 | 7544 | 25046 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_864x480.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mot/jde/jde_darknet53_30e_864x480.yml) |
| DarkNet53 | 576x320 | 63.7 | 64.4 | 1310 | 6782 | 31964 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_576x320.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mot/jde/jde_darknet53_30e_576x320.yml) |
### JDE Results on MOT-16 Test Set
| backbone | input shape | MOTA | IDF1 | IDS | FP | FN | FPS | download | config |
| :----------------- | :------- | :----: | :----: | :---: | :----: | :---: | :---: | :---: | :---: |
| DarkNet53(paper) | 1088x608 | 64.4 | 55.8 | 1544 | - | - | - | - | - |
| DarkNet53 | 1088x608 | 64.6 | 58.5 | 1864 | 10550 | 52088 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_1088x608.yml) |
| DarkNet53 | 1088x608 | 64.6 | 58.5 | 1864 | 10550 | 52088 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mot/jde/jde_darknet53_30e_1088x608.yml) |
| DarkNet53(paper) | 864x480 | 62.1 | 56.9 | 1608 | - | - | - | - | - |
| DarkNet53 | 864x480 | 63.2 | 57.7 | 1966 | 10070 | 55081 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_864x480.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_864x480.yml) |
| DarkNet53 | 576x320 | 59.1 | 56.4 | 1911 | 10923 | 61789 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_576x320.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_576x320.yml) |
| DarkNet53 | 864x480 | 63.2 | 57.7 | 1966 | 10070 | 55081 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_864x480.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mot/jde/jde_darknet53_30e_864x480.yml) |
| DarkNet53 | 576x320 | 59.1 | 56.4 | 1911 | 10923 | 61789 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_576x320.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mot/jde/jde_darknet53_30e_576x320.yml) |
**Notes:**
- JDE used 8 GPUs for training and mini-batch size as 4 on each GPU, and trained for 30 epoches.
......
......@@ -29,9 +29,9 @@ PP-Tracking 提供了AI Studio公开项目案例,教程请参考[PP-Tracking
| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 |
| :----------------- | :------- | :----: | :----: | :---: | :----: | :---: | :---: | :---: | :---: |
| DarkNet53 | 1088x608 | 72.0 | 66.9 | 1397 | 7274 | 22209 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_1088x608.yml) |
| DarkNet53 | 864x480 | 69.1 | 64.7 | 1539 | 7544 | 25046 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_864x480.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_864x480.yml) |
| DarkNet53 | 576x320 | 63.7 | 64.4 | 1310 | 6782 | 31964 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_576x320.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_576x320.yml) |
| DarkNet53 | 1088x608 | 72.0 | 66.9 | 1397 | 7274 | 22209 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mot/jde/jde_darknet53_30e_1088x608.yml) |
| DarkNet53 | 864x480 | 69.1 | 64.7 | 1539 | 7544 | 25046 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_864x480.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mot/jde/jde_darknet53_30e_864x480.yml) |
| DarkNet53 | 576x320 | 63.7 | 64.4 | 1310 | 6782 | 31964 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_576x320.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mot/jde/jde_darknet53_30e_576x320.yml) |
### JDE在MOT-16 Test Set上结果
......@@ -39,10 +39,10 @@ PP-Tracking 提供了AI Studio公开项目案例,教程请参考[PP-Tracking
| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 |
| :----------------- | :------- | :----: | :----: | :---: | :----: | :---: | :---: | :---: | :---: |
| DarkNet53(paper) | 1088x608 | 64.4 | 55.8 | 1544 | - | - | - | - | - |
| DarkNet53 | 1088x608 | 64.6 | 58.5 | 1864 | 10550 | 52088 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_1088x608.yml) |
| DarkNet53 | 1088x608 | 64.6 | 58.5 | 1864 | 10550 | 52088 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mot/jde/jde_darknet53_30e_1088x608.yml) |
| DarkNet53(paper) | 864x480 | 62.1 | 56.9 | 1608 | - | - | - | - | - |
| DarkNet53 | 864x480 | 63.2 | 57.7 | 1966 | 10070 | 55081 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_864x480.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_864x480.yml) |
| DarkNet53 | 576x320 | 59.1 | 56.4 | 1911 | 10923 | 61789 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_576x320.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_576x320.yml) |
| DarkNet53 | 864x480 | 63.2 | 57.7 | 1966 | 10070 | 55081 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_864x480.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mot/jde/jde_darknet53_30e_864x480.yml) |
| DarkNet53 | 576x320 | 59.1 | 56.4 | 1911 | 10923 | 61789 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_576x320.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mot/jde/jde_darknet53_30e_576x320.yml) |
**注意:**
- JDE使用8个GPU进行训练,每个GPU上batch size为4,训练了30个epoch。
......
......@@ -5,7 +5,7 @@ We provide some models implemented by PaddlePaddle to detect objects in specific
| Task | Algorithm | Box AP | Download | Configs |
|:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: |:------:|
| Pedestrian Detection | YOLOv3 | 51.8 | [model](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/pedestrian/pedestrian_yolov3_darknet.yml) |
| Pedestrian Detection | YOLOv3 | 51.8 | [model](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/pedestrian/pedestrian_yolov3_darknet.yml) |
## Pedestrian Detection
......@@ -17,7 +17,7 @@ The network for detecting vehicles is YOLOv3, the backbone of which is Dacknet53
### 2. Configuration for training
PaddleDetection provides users with a configuration file [yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for pedestrian detection:
PaddleDetection provides users with a configuration file [yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for pedestrian detection:
* num_classes: 1
* dataset_dir: dataset/pedestrian
......
......@@ -5,7 +5,7 @@
| 任务 | 算法 | 精度(Box AP) | 下载 | 配置文件 |
|:---------------------|:---------:|:------:| :---------------------------------------------------------------------------------: | :------:|
| 行人检测 | YOLOv3 | 51.8 | [下载链接](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/pedestrian/pedestrian_yolov3_darknet.yml) |
| 行人检测 | YOLOv3 | 51.8 | [下载链接](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/pedestrian/pedestrian_yolov3_darknet.yml) |
## 行人检测(Pedestrian Detection)
......@@ -18,7 +18,7 @@ Backbone为Dacknet53的YOLOv3。
### 2. 训练参数配置
PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml),与之相比,在进行行人检测的模型训练时,我们对以下参数进行了修改:
PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml),与之相比,在进行行人检测的模型训练时,我们对以下参数进行了修改:
* num_classes: 1
* dataset_dir: dataset/pedestrian
......
......@@ -45,7 +45,6 @@ PP-PicoDet模型有如下特点:
| PicoDet-L | 416*416 | 39.4 | 55.7 | 5.80 | 7.10 | 22.1ms | 42.23ms | [model](https://paddledet.bj.bcebos.com/models/picodet_l_416_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_416_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/picodet_l_416_coco_lcnet.yml) |
| PicoDet-L | 640*640 | 42.6 | 59.2 | 5.80 | 16.81 | 43.1ms | 108.1ms | [model](https://paddledet.bj.bcebos.com/models/picodet_l_640_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_640_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/picodet_l_640_coco_lcnet.yml) |
<details open>
<summary><b>注意事项:</b></summary>
......
......@@ -2,23 +2,23 @@
| Model | Input size | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params<br><sup>(M) | FLOPS<br><sup>(G) | Latency<sup><small>[NCNN](#latency)</small><sup><br><sup>(ms) | Latency<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) | Download | Config |
| :-------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: | :-----------------------------: | :----------------------------------------: | :--------------------------------------- |
| PicoDet-S | 320*320 | 27.1 | 41.4 | 0.99 | 0.73 | 8.13 | **6.65** | [model](https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_320_coco.yml) |
| PicoDet-S | 416*416 | 30.7 | 45.8 | 0.99 | 1.24 | 12.37 | **9.82** | [model](https://paddledet.bj.bcebos.com/models/picodet_s_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_416_coco.yml) |
| PicoDet-M | 320*320 | 30.9 | 45.7 | 2.15 | 1.48 | 11.27 | **9.61** | [model](https://paddledet.bj.bcebos.com/models/picodet_m_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_320_coco.yml) |
| PicoDet-M | 416*416 | 34.8 | 50.5 | 2.15 | 2.50 | 17.39 | **15.88** | [model](https://paddledet.bj.bcebos.com/models/picodet_m_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_416_coco.yml) |
| PicoDet-L | 320*320 | 32.9 | 48.2 | 3.30 | 2.23 | 15.26 | **13.42** | [model](https://paddledet.bj.bcebos.com/models/picodet_l_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_l_320_coco.yml) |
| PicoDet-L | 416*416 | 36.6 | 52.5 | 3.30 | 3.76 | 23.36 | **21.85** | [model](https://paddledet.bj.bcebos.com/models/picodet_l_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_l_416_coco.yml) |
| PicoDet-L | 640*640 | 40.9 | 57.6 | 3.30 | 8.91 | 54.11 | **50.55** | [model](https://paddledet.bj.bcebos.com/models/picodet_l_640_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_640_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_l_640_coco.yml) |
| PicoDet-S | 320*320 | 27.1 | 41.4 | 0.99 | 0.73 | 8.13 | **6.65** | [model](https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/picodet_s_320_coco.yml) |
| PicoDet-S | 416*416 | 30.7 | 45.8 | 0.99 | 1.24 | 12.37 | **9.82** | [model](https://paddledet.bj.bcebos.com/models/picodet_s_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/picodet_s_416_coco.yml) |
| PicoDet-M | 320*320 | 30.9 | 45.7 | 2.15 | 1.48 | 11.27 | **9.61** | [model](https://paddledet.bj.bcebos.com/models/picodet_m_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/picodet_m_320_coco.yml) |
| PicoDet-M | 416*416 | 34.8 | 50.5 | 2.15 | 2.50 | 17.39 | **15.88** | [model](https://paddledet.bj.bcebos.com/models/picodet_m_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/picodet_m_416_coco.yml) |
| PicoDet-L | 320*320 | 32.9 | 48.2 | 3.30 | 2.23 | 15.26 | **13.42** | [model](https://paddledet.bj.bcebos.com/models/picodet_l_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/picodet_l_320_coco.yml) |
| PicoDet-L | 416*416 | 36.6 | 52.5 | 3.30 | 3.76 | 23.36 | **21.85** | [model](https://paddledet.bj.bcebos.com/models/picodet_l_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/picodet_l_416_coco.yml) |
| PicoDet-L | 640*640 | 40.9 | 57.6 | 3.30 | 8.91 | 54.11 | **50.55** | [model](https://paddledet.bj.bcebos.com/models/picodet_l_640_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_640_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/picodet_l_640_coco.yml) |
#### More Configs
| Model | Input size | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params<br><sup>(M) | FLOPS<br><sup>(G) | Latency<sup><small>[NCNN](#latency)</small><sup><br><sup>(ms) | Latency<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) | Download | Config |
| :--------------------------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: | :-----------------------------: | :----------------------------------------: | :--------------------------------------- |
| PicoDet-Shufflenetv2 1x | 416*416 | 30.0 | 44.6 | 1.17 | 1.53 | 15.06 | **10.63** | [model](https://paddledet.bj.bcebos.com/models/picodet_shufflenetv2_1x_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_shufflenetv2_1x_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/more_config/picodet_shufflenetv2_1x_416_coco.yml) |
| PicoDet-MobileNetv3-large 1x | 416*416 | 35.6 | 52.0 | 3.55 | 2.80 | 20.71 | **17.88** | [model](https://paddledet.bj.bcebos.com/models/picodet_mobilenetv3_large_1x_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_mobilenetv3_large_1x_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/more_config/picodet_mobilenetv3_large_1x_416_coco.yml) |
| PicoDet-LCNet 1.5x | 416*416 | 36.3 | 52.2 | 3.10 | 3.85 | 21.29 | **20.8** | [model](https://paddledet.bj.bcebos.com/models/picodet_lcnet_1_5x_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_lcnet_1_5x_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/more_config/picodet_lcnet_1_5x_416_coco.yml) |
| PicoDet-LCNet 1.5x | 640*640 | 40.6 | 57.4 | 3.10 | - | - | - | [model](https://paddledet.bj.bcebos.com/models/picodet_lcnet_1_5x_640_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_lcnet_1_5x_640_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/more_config/picodet_lcnet_1_5x_640_coco.yml) |
| PicoDet-R18 | 640*640 | 40.7 | 57.2 | 11.10 | - | - | - | [model](https://paddledet.bj.bcebos.com/models/picodet_r18_640_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_r18_640_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/more_config/picodet_r18_640_coco.yml) |
| PicoDet-Shufflenetv2 1x | 416*416 | 30.0 | 44.6 | 1.17 | 1.53 | 15.06 | **10.63** | [model](https://paddledet.bj.bcebos.com/models/picodet_shufflenetv2_1x_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_shufflenetv2_1x_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/more_config/picodet_shufflenetv2_1x_416_coco.yml) |
| PicoDet-MobileNetv3-large 1x | 416*416 | 35.6 | 52.0 | 3.55 | 2.80 | 20.71 | **17.88** | [model](https://paddledet.bj.bcebos.com/models/picodet_mobilenetv3_large_1x_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_mobilenetv3_large_1x_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/more_config/picodet_mobilenetv3_large_1x_416_coco.yml) |
| PicoDet-LCNet 1.5x | 416*416 | 36.3 | 52.2 | 3.10 | 3.85 | 21.29 | **20.8** | [model](https://paddledet.bj.bcebos.com/models/picodet_lcnet_1_5x_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_lcnet_1_5x_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/more_config/picodet_lcnet_1_5x_416_coco.yml) |
| PicoDet-LCNet 1.5x | 640*640 | 40.6 | 57.4 | 3.10 | - | - | - | [model](https://paddledet.bj.bcebos.com/models/picodet_lcnet_1_5x_640_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_lcnet_1_5x_640_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/more_config/picodet_lcnet_1_5x_640_coco.yml) |
| PicoDet-R18 | 640*640 | 40.7 | 57.2 | 11.10 | - | - | - | [model](https://paddledet.bj.bcebos.com/models/picodet_r18_640_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_r18_640_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet/more_config/picodet_r18_640_coco.yml) |
<details open>
<summary><b>Table Notes:</b></summary>
......
# 非结构化稀疏在 PicoDet 上的应用教程
## 1. 介绍
在模型压缩中,常见的稀疏方式为结构化稀疏和非结构化稀疏,前者在某个特定维度(特征通道、卷积核等等)上对卷积、矩阵乘法进行剪枝操作,然后生成一个更小的模型结构,这样可以复用已有的卷积、矩阵乘计算,无需特殊实现推理算子;后者以每一个参数为单元进行稀疏化,然而并不会改变参数矩阵的形状,所以更依赖于推理库、硬件对于稀疏后矩阵运算的加速能力。我们在 PP-PicoDet (以下简称PicoDet) 模型上运用了非结构化稀疏技术,在精度损失较小时,获得了在 ARM CPU 端推理的显著性能提升。本文档会介绍如何非结构化稀疏训练 PicoDet,关于非结构化稀疏的更多介绍请参照[这里](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/dygraph/unstructured_pruning)
在模型压缩中,常见的稀疏方式为结构化稀疏和非结构化稀疏,前者在某个特定维度(特征通道、卷积核等等)上对卷积、矩阵乘法进行剪枝操作,然后生成一个更小的模型结构,这样可以复用已有的卷积、矩阵乘计算,无需特殊实现推理算子;后者以每一个参数为单元进行稀疏化,然而并不会改变参数矩阵的形状,所以更依赖于推理库、硬件对于稀疏后矩阵运算的加速能力。我们在 PP-PicoDet (以下简称PicoDet) 模型上运用了非结构化稀疏技术,在精度损失较小时,获得了在 ARM CPU 端推理的显著性能提升。本文档会介绍如何非结构化稀疏训练 PicoDet,关于非结构化稀疏的更多介绍请参照[这里](https://github.com/PaddlePaddle/PaddleSlim/tree/release/2.4/demo/dygraph/unstructured_pruning)
## 2. 版本要求
```bash
......@@ -113,9 +113,9 @@ paddle_lite_opt --model_dir=inference_model/picodet_m_320_coco --valid_targets=a
| Model | Input size | Sparsity | mAP<sup>val<br>0.5:0.95 | Size<br><sup>(MB) | Latency single-thread<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) | speed-up single-thread | Latency 4-thread<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) | speed-up 4-thread | Download | SlimConfig |
| :-------- | :--------: |:--------: | :---------------------: | :----------------: | :----------------: |:----------------: | :---------------: | :-----------------------------: | :-----------------------------: | :----------------------------------------: |
| PicoDet-m-1.0 | 320*320 | 0 | 30.9 | 8.9 | 127 | 0 | 43 | 0 | [model](https://paddledet.bj.bcebos.com/models/picodet_m_320_coco.pdparams)&#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_m_320_coco.yml)|
| PicoDet-m-1.0 | 320*320 | 75% | 29.4 | 5.6 | **80** | 58% | **32** | 34% | [model](https://paddledet.bj.bcebos.com/models/slim/picodet_m_320__coco_sparse_75.pdparams)&#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320__coco_sparse_75.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/slim/prune/picodet_m_unstructured_prune_75.yml)|
| PicoDet-m-1.0 | 320*320 | 75% | 29.4 | 5.6 | **80** | 58% | **32** | 34% | [model](https://paddledet.bj.bcebos.com/models/slim/picodet_m_320__coco_sparse_75.pdparams)&#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320__coco_sparse_75.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/slim/prune/picodet_m_unstructured_prune_75.yml)|
| PicoDet-s-1.0 | 320*320 | 0 | 27.1 | 4.6 | 68 | 0 | 26 | 0 | [model](https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_s_320_coco.yml)|
| PicoDet-m-1.0 | 320*320 | 85% | 27.6 | 4.1 | **65** | 96% | **27** | 59% | [model](https://paddledet.bj.bcebos.com/models/slim/picodet_m_320__coco_sparse_85.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320__coco_sparse_85.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/slim/prune/picodet_m_unstructured_prune_85.yml)|
| PicoDet-m-1.0 | 320*320 | 85% | 27.6 | 4.1 | **65** | 96% | **27** | 59% | [model](https://paddledet.bj.bcebos.com/models/slim/picodet_m_320__coco_sparse_85.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320__coco_sparse_85.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/slim/prune/picodet_m_unstructured_prune_85.yml)|
**注意:**
- 上述模型体积是**部署模型体积**,即 PaddleLite 转换得到的 *.nb 文件的体积。
......
......@@ -41,25 +41,25 @@ PP-YOLO and PP-YOLOv2 improved performance and speed of YOLOv3 with following me
| Model | GPU number | images/GPU | backbone | input shape | Box AP<sup>val</sup> | Box AP<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config |
|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :------: |
| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 43.9 | 44.4 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.1 | 42.5 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 38.9 | 39.3 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 512 | 29.2 | 29.5 | 357.1 | 657.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet50vd | 640 | 49.1 | 49.5 | 68.9 | 106.5 | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet101vd | 640 | 49.7 | 50.3 | 49.5 | 87.0 | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolov2_r101vd_dcn_365e_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 43.9 | 44.4 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.1 | 42.5 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 38.9 | 39.3 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 512 | 29.2 | 29.5 | 357.1 | 657.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet50vd | 640 | 49.1 | 49.5 | 68.9 | 106.5 | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet101vd | 640 | 49.7 | 50.3 | 49.5 | 87.0 | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolov2_r101vd_dcn_365e_coco.yml) |
**Notes:**
- PP-YOLO is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset,Box AP<sup>test</sup> is evaluation results of `mAP(IoU=0.5:0.95)`.
- PP-YOLO used 8 GPUs for training and mini-batch size as 24 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ).
- PP-YOLO used 8 GPUs for training and mini-batch size as 24 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/FAQ).
- PP-YOLO inference speed is tesed on single Tesla V100 with batch size as 1, CUDA 10.2, CUDNN 7.5.1, TensorRT 5.1.2.2 in TensorRT mode.
- PP-YOLO FP32 inference speed testing uses inference model exported by `tools/export_model.py` and benchmarked by running `depoly/python/infer.py` with `--run_benchmark`. All testing results do not contains the time cost of data reading and post-processing(NMS), which is same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) in testing method.
- TensorRT FP16 inference speed testing exclude the time cost of bounding-box decoding(`yolo_box`) part comparing with FP32 testing above, which means that data reading, bounding-box decoding and post-processing(NMS) is excluded(test method same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) too)
......@@ -69,26 +69,26 @@ PP-YOLO and PP-YOLOv2 improved performance and speed of YOLOv3 with following me
| Model | GPU number | images/GPU | Model Size | input shape | Box AP<sup>val</sup> | Box AP50<sup>val</sup> | Kirin 990 1xCore(FPS) | download | config |
|:----------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :--------------------: | :--------------------: | :------: | :------: |
| PP-YOLO_MobileNetV3_large | 4 | 32 | 28MB | 320 | 23.2 | 42.6 | 14.1 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 16MB | 320 | 17.2 | 33.8 | 21.5 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_small_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_small_coco.yml) |
| PP-YOLO_MobileNetV3_large | 4 | 32 | 28MB | 320 | 23.2 | 42.6 | 14.1 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_mbv3_large_coco.yml) |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 16MB | 320 | 17.2 | 33.8 | 21.5 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_small_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_mbv3_small_coco.yml) |
**Notes:**
- PP-YOLO_MobileNetV3 is trained on COCO train2017 datast and evaluated on val2017 dataset,Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5:0.95)`, Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5)`.
- PP-YOLO_MobileNetV3 used 4 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ).
- PP-YOLO_MobileNetV3 used 4 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/FAQ).
- PP-YOLO_MobileNetV3 inference speed is tested on Kirin 990 with 1 thread.
### PP-YOLO tiny
| Model | GPU number | images/GPU | Model Size | Post Quant Model Size | input shape | Box AP<sup>val</sup> | Kirin 990 4xCore(FPS) | download | config | post quant model |
|:----------------------------:|:-------:|:-------------:|:----------:| :-------------------: | :---------: | :------------------: | :-------------------: | :------: | :----: | :--------------: |
| PP-YOLO tiny | 8 | 32 | 4.2MB | **1.3M** | 320 | 20.6 | 92.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_650e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_tiny_650e_coco.yml) | [inference model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_quant.tar) |
| PP-YOLO tiny | 8 | 32 | 4.2MB | **1.3M** | 416 | 22.7 | 65.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_650e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_tiny_650e_coco.yml) | [inference model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_quant.tar) |
| PP-YOLO tiny | 8 | 32 | 4.2MB | **1.3M** | 320 | 20.6 | 92.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_650e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_tiny_650e_coco.yml) | [inference model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_quant.tar) |
| PP-YOLO tiny | 8 | 32 | 4.2MB | **1.3M** | 416 | 22.7 | 65.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_650e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_tiny_650e_coco.yml) | [inference model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_quant.tar) |
**Notes:**
- PP-YOLO-tiny is trained on COCO train2017 datast and evaluated on val2017 dataset,Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5:0.95)`, Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5)`.
- PP-YOLO-tiny used 8 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ/README.md).
- PP-YOLO-tiny used 8 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/FAQ/README.md).
- PP-YOLO-tiny inference speed is tested on Kirin 990 with 4 threads by arm8
- we alse provide PP-YOLO-tiny post quant inference model, which can compress model to **1.3MB** with nearly no inference on inference speed and performance
......@@ -98,9 +98,9 @@ PP-YOLO trained on Pascal VOC dataset as follows:
| Model | GPU number | images/GPU | backbone | input shape | Box AP50<sup>val</sup> | download | config |
|:------------------:|:----------:|:----------:|:----------:| :----------:| :--------------------: | :------: | :-----: |
| PP-YOLO | 8 | 12 | ResNet50vd | 608 | 84.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 416 | 84.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 320 | 82.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 608 | 84.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 416 | 84.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 320 | 82.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
## Getting Start
......@@ -212,7 +212,7 @@ Optimizing method and ablation experiments of PP-YOLO compared with YOLOv3.
- Performance and inference spedd are measure with input shape as 608
- All models are trained on COCO train2017 datast and evaluated on val2017 & test-dev2017 dataset,`Box AP` is evaluation results as `mAP(IoU=0.5:0.95)`.
- Inference speed is tested on single Tesla V100 with batch size as 1 following test method and environment configuration in benchmark above.
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) with mAP as 39.0 is optimized YOLOv3 model in PaddleDetection,see [YOLOv3](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/README.md) for details.
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) with mAP as 39.0 is optimized YOLOv3 model in PaddleDetection,see [YOLOv3](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/yolov3/README.md) for details.
## Citation
......
......@@ -41,24 +41,24 @@ PP-YOLO和PP-YOLOv2从如下方面优化和提升YOLOv3模型的精度和速度
| 模型 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP<sup>val</sup> | Box AP<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 |
|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :------: |
| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 43.9 | 44.4 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.1 | 42.5 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 38.9 | 39.3 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 512 | 29.2 | 29.5 | 357.1 | 657.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet50vd | 640 | 49.1 | 49.5 | 68.9 | 106.5 | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet101vd | 640 | 49.7 | 50.3 | 49.5 | 87.0 | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolov2_r101vd_dcn_365e_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 43.9 | 44.4 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.1 | 42.5 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 38.9 | 39.3 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 512 | 29.2 | 29.5 | 357.1 | 657.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet50vd | 640 | 49.1 | 49.5 | 68.9 | 106.5 | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet101vd | 640 | 49.7 | 50.3 | 49.5 | 87.0 | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolov2_r101vd_dcn_365e_coco.yml) |
**注意:**
- PP-YOLO模型使用COCO数据集中train2017作为训练集,使用val2017和test-dev2017作为测试集,Box AP<sup>test</sup>`mAP(IoU=0.5:0.95)`评估结果。
- PP-YOLO模型训练过程中使用8 GPUs,每GPU batch size为24进行训练,如训练GPU数和batch size不使用上述配置,须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ)调整学习率和迭代次数。
- PP-YOLO模型训练过程中使用8 GPUs,每GPU batch size为24进行训练,如训练GPU数和batch size不使用上述配置,须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/FAQ)调整学习率和迭代次数。
- PP-YOLO模型推理速度测试采用单卡V100,batch size=1进行测试,使用CUDA 10.2, CUDNN 7.5.1,TensorRT推理速度测试使用TensorRT 5.1.2.2。
- PP-YOLO模型FP32的推理速度测试数据为使用`tools/export_model.py`脚本导出模型后,使用`deploy/python/infer.py`脚本中的`--run_benchnark`参数使用Paddle预测库进行推理速度benchmark测试结果, 且测试的均为不包含数据预处理和模型输出后处理(NMS)的数据(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
- TensorRT FP16的速度测试相比于FP32去除了`yolo_box`(bbox解码)部分耗时,即不包含数据预处理,bbox解码和NMS(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
......@@ -67,22 +67,22 @@ PP-YOLO和PP-YOLOv2从如下方面优化和提升YOLOv3模型的精度和速度
| 模型 | GPU个数 | 每GPU图片个数 | 模型体积 | 输入尺寸 | Box AP<sup>val</sup> | Box AP50<sup>val</sup> | Kirin 990 1xCore (FPS) | 模型下载 | 配置文件 |
|:----------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :--------------------: | :--------------------: | :------: | :------: |
| PP-YOLO_MobileNetV3_large | 4 | 32 | 28MB | 320 | 23.2 | 42.6 | 14.1 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 16MB | 320 | 17.2 | 33.8 | 21.5 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_small_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_small_coco.yml) |
| PP-YOLO_MobileNetV3_large | 4 | 32 | 28MB | 320 | 23.2 | 42.6 | 14.1 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_mbv3_large_coco.yml) |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 16MB | 320 | 17.2 | 33.8 | 21.5 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_small_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_mbv3_small_coco.yml) |
- PP-YOLO_MobileNetV3 模型使用COCO数据集中train2017作为训练集,使用val2017作为测试集,Box AP<sup>val</sup>`mAP(IoU=0.5:0.95)`评估结果, Box AP50<sup>val</sup>`mAP(IoU=0.5)`评估结果。
- PP-YOLO_MobileNetV3 模型训练过程中使用4GPU,每GPU batch size为32进行训练,如训练GPU数和batch size不使用上述配置,须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ)调整学习率和迭代次数。
- PP-YOLO_MobileNetV3 模型训练过程中使用4GPU,每GPU batch size为32进行训练,如训练GPU数和batch size不使用上述配置,须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/FAQ)调整学习率和迭代次数。
- PP-YOLO_MobileNetV3 模型推理速度测试环境配置为麒麟990芯片单线程。
### PP-YOLO tiny模型
| 模型 | GPU 个数 | 每GPU图片个数 | 模型体积 | 后量化模型体积 | 输入尺寸 | Box AP<sup>val</sup> | Kirin 990 1xCore (FPS) | 模型下载 | 配置文件 | 量化后模型 |
|:----------------------------:|:----------:|:-------------:| :--------: | :------------: | :----------:| :------------------: | :--------------------: | :------: | :------: | :--------: |
| PP-YOLO tiny | 8 | 32 | 4.2MB | **1.3M** | 320 | 20.6 | 92.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_650e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_tiny_650e_coco.yml) | [预测模型](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_quant.tar) |
| PP-YOLO tiny | 8 | 32 | 4.2MB | **1.3M** | 416 | 22.7 | 65.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_650e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_tiny_650e_coco.yml) | [预测模型](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_quant.tar) |
| PP-YOLO tiny | 8 | 32 | 4.2MB | **1.3M** | 320 | 20.6 | 92.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_650e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_tiny_650e_coco.yml) | [预测模型](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_quant.tar) |
| PP-YOLO tiny | 8 | 32 | 4.2MB | **1.3M** | 416 | 22.7 | 65.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_650e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_tiny_650e_coco.yml) | [预测模型](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_quant.tar) |
- PP-YOLO-tiny 模型使用COCO数据集中train2017作为训练集,使用val2017作为测试集,Box AP<sup>val</sup>`mAP(IoU=0.5:0.95)`评估结果, Box AP50<sup>val</sup>`mAP(IoU=0.5)`评估结果。
- PP-YOLO-tiny 模型训练过程中使用8GPU,每GPU batch size为32进行训练,如训练GPU数和batch size不使用上述配置,须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ/README.md)调整学习率和迭代次数。
- PP-YOLO-tiny 模型训练过程中使用8GPU,每GPU batch size为32进行训练,如训练GPU数和batch size不使用上述配置,须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/FAQ/README.md)调整学习率和迭代次数。
- PP-YOLO-tiny 模型推理速度测试环境配置为麒麟990芯片4线程,arm8架构。
- 我们也提供的PP-YOLO-tiny的后量化压缩模型,将模型体积压缩到**1.3M**,对精度和预测速度基本无影响
......@@ -92,9 +92,9 @@ PP-YOLO在Pascal VOC数据集上训练模型如下:
| 模型 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP50<sup>val</sup> | 模型下载 | 配置文件 |
|:------------------:|:-------:|:-------------:|:----------:| :----------:| :--------------------: | :------: | :-----: |
| PP-YOLO | 8 | 12 | ResNet50vd | 608 | 84.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 416 | 84.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 320 | 82.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 608 | 84.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 416 | 84.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 320 | 82.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
## 使用说明
......@@ -205,7 +205,7 @@ PP-YOLO模型相对于YOLOv3模型优化项消融实验数据如下表所示。
- 精度与推理速度数据均为使用输入图像尺寸为608的测试结果
- Box AP为在COCO train2017数据集训练,val2017和test-dev2017数据集上评估`mAP(IoU=0.5:0.95)`数据
- 推理速度为单卡V100上,batch size=1, 使用上述benchmark测试方法的测试结果,测试环境配置为CUDA 10.2,CUDNN 7.5.1
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml)精度38.9为PaddleDetection优化后的YOLOv3模型,可参见[YOLOv3](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/README.md)
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml)精度38.9为PaddleDetection优化后的YOLOv3模型,可参见[YOLOv3](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/yolov3/README.md)
## 引用
......
......@@ -26,15 +26,15 @@ PP-YOLOE is composed of following methods:
## Model Zoo
| Model | GPU number | images/GPU | backbone | input shape | Box AP<sup>val</sup> | Box AP<sup>test</sup> | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config |
|:------------------------:|:-------:|:----------:|:----------:| :-------:| :------------------: | :-------------------: |:---------:|:--------:| :------------: | :---------------------: | :------: | :------: |
| PP-YOLOE-s | 8 | 32 | cspresnet-s | 640 | 42.7 | 43.1 | 7.93 | 17.36 | 208.3 | 333.3 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml) |
| PP-YOLOE-m | 8 | 28 | cspresnet-m | 640 | 48.6 | 48.9 | 23.43 | 49.91 | 123.4 | 208.3 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml) |
| PP-YOLOE-l | 8 | 20 | cspresnet-l | 640 | 50.9 | 51.4 | 52.20 | 110.07 | 78.1 | 149.2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml) |
| PP-YOLOE-x | 8 | 16 | cspresnet-x | 640 | 51.9 | 52.2 | 98.42 | 206.59 | 45.0 | 95.2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml) |
| PP-YOLOE-s | 8 | 32 | cspresnet-s | 640 | 42.7 | 43.1 | 7.93 | 17.36 | 208.3 | 333.3 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml) |
| PP-YOLOE-m | 8 | 28 | cspresnet-m | 640 | 48.6 | 48.9 | 23.43 | 49.91 | 123.4 | 208.3 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml) |
| PP-YOLOE-l | 8 | 20 | cspresnet-l | 640 | 50.9 | 51.4 | 52.20 | 110.07 | 78.1 | 149.2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml) |
| PP-YOLOE-x | 8 | 16 | cspresnet-x | 640 | 51.9 | 52.2 | 98.42 | 206.59 | 45.0 | 95.2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml) |
**Notes:**
- PP-YOLOE is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset,Box AP<sup>test</sup> is evaluation results of `mAP(IoU=0.5:0.95)`.
- PP-YOLOE used 8 GPUs for mixed precision training, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ).
- PP-YOLOE used 8 GPUs for mixed precision training, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/FAQ).
- PP-YOLOE inference speed is tesed on single Tesla V100 with batch size as 1, CUDA 10.2, CUDNN 7.6.5, TensorRT 6.0.1.8 in TensorRT mode.
- PP-YOLOE inference speed testing uses inference model exported by `tools/export_model.py` with `-o exclude_nms=True` and benchmarked by running `depoly/python/infer.py` with `--run_benchmark`. All testing results do not contains the time cost of data reading and post-processing(NMS), which is same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) in testing method.
- If you set `--run_benchmark=True`,you should install these dependencies at first, `pip install pynvml psutil GPUtil`.
......
......@@ -26,15 +26,15 @@ PP-YOLOE由以下方法组成
## 模型库
| 模型 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP<sup>val</sup> | Box AP<sup>test</sup> | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 |
|:------------------------:|:-------:|:--------:|:----------:| :-------:| :------------------: | :-------------------: |:---------:|:--------:|:---------------:| :---------------------: | :------: | :------: |
| PP-YOLOE-s | 8 | 32 | cspresnet-s | 640 | 42.7 | 43.1 | 7.93 | 17.36 | 208.3 | 333.3 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml) |
| PP-YOLOE-m | 8 | 28 | cspresnet-m | 640 | 48.6 | 48.9 | 23.43 | 49.91 | 123.4 | 208.3 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml) |
| PP-YOLOE-l | 8 | 20 | cspresnet-l | 640 | 50.9 | 51.4 | 52.20 | 110.07 | 78.1 | 149.2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml) |
| PP-YOLOE-x | 8 | 16 | cspresnet-x | 640 | 51.9 | 52.2 | 98.42 | 206.59 | 45.0 | 95.2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml) |
| PP-YOLOE-s | 8 | 32 | cspresnet-s | 640 | 42.7 | 43.1 | 7.93 | 17.36 | 208.3 | 333.3 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml) |
| PP-YOLOE-m | 8 | 28 | cspresnet-m | 640 | 48.6 | 48.9 | 23.43 | 49.91 | 123.4 | 208.3 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml) |
| PP-YOLOE-l | 8 | 20 | cspresnet-l | 640 | 50.9 | 51.4 | 52.20 | 110.07 | 78.1 | 149.2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml) |
| PP-YOLOE-x | 8 | 16 | cspresnet-x | 640 | 51.9 | 52.2 | 98.42 | 206.59 | 45.0 | 95.2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml) |
**注意:**
- PP-YOLOE模型使用COCO数据集中train2017作为训练集,使用val2017和test-dev2017作为测试集,Box AP<sup>test</sup>`mAP(IoU=0.5:0.95)`评估结果。
- PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练,如果训练GPU数和batch size不使用上述配置,须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ)调整学习率和迭代次数。
- PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练,如果训练GPU数和batch size不使用上述配置,须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/FAQ)调整学习率和迭代次数。
- PP-YOLOE模型推理速度测试采用单卡V100,batch size=1进行测试,使用CUDA 10.2, CUDNN 7.6.5,TensorRT推理速度测试使用TensorRT 6.0.1.8。
- PP-YOLOE推理速度测试使用`tools/export_model.py`并设置`-o exclude_nms=True`脚本导出的模型,并用`deploy/python/infer.py`设置`--run_benchnark`参数得到。测试结果均为不包含数据预处理和模型输出后处理(NMS)的数据(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
- 如果你设置了`--run_benchnark=True`, 你首先需要安装以下依赖`pip install pynvml psutil GPUtil`
......
......@@ -9,4 +9,4 @@
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | Mask AP | 下载 | 配置文件 |
| :---------------------- | :-------------: | :-------: | :-----: | :------------: | :----: | :-----: | :-------------: | :-----: |
| ResNet50-vd-FPN-Dcnv2 | Faster | 2 | 3x | 61.425 | 41.5 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_enhance_3x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rcnn_enhance/faster_rcnn_enhance_3x_coco.yml) |
| ResNet50-vd-FPN-Dcnv2 | Faster | 2 | 3x | 61.425 | 41.5 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_enhance_3x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/rcnn_enhance/faster_rcnn_enhance_3x_coco.yml) |
......@@ -9,4 +9,4 @@
| Backbone | Network type | Number of images per GPU | Learning rate strategy | Inferring time(fps) | Box AP | Mask AP | Download | Configuration File |
| :-------------------- | :----------: | :----------------------: | :--------------------: | :-----------------: | :----: | :-----: | :---------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------: |
| ResNet50-vd-FPN-Dcnv2 | Faster | 2 | 3x | 61.425 | 41.5 | - | [link](https://paddledet.bj.bcebos.com/models/faster_rcnn_enhance_3x_coco.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rcnn_enhance/faster_rcnn_enhance_3x_coco.yml) |
| ResNet50-vd-FPN-Dcnv2 | Faster | 2 | 3x | 61.425 | 41.5 | - | [link](https://paddledet.bj.bcebos.com/models/faster_rcnn_enhance_3x_coco.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/rcnn_enhance/faster_rcnn_enhance_3x_coco.yml) |
......@@ -30,8 +30,8 @@
| Backbone | Type | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | Configs |
| :---------------------- | :------------- | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
| Res2Net50-FPN | Faster | 2 | 1x | - | 40.6 | - | [model](https://paddledet.bj.bcebos.com/models/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco.yml) |
| Res2Net50-FPN | Mask | 2 | 2x | - | 42.4 | 38.1 | [model](https://paddledet.bj.bcebos.com/models/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/res2net/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.yml) |
| Res2Net50-vd-FPN | Mask | 2 | 2x | - | 42.6 | 38.1 | [model](https://paddledet.bj.bcebos.com/models/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.yml) |
| Res2Net50-FPN | Faster | 2 | 1x | - | 40.6 | - | [model](https://paddledet.bj.bcebos.com/models/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco.yml) |
| Res2Net50-FPN | Mask | 2 | 2x | - | 42.4 | 38.1 | [model](https://paddledet.bj.bcebos.com/models/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/res2net/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.yml) |
| Res2Net50-vd-FPN | Mask | 2 | 2x | - | 42.6 | 38.1 | [model](https://paddledet.bj.bcebos.com/models/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.yml) |
Note: all the above models are trained with 8 gpus.
......@@ -103,18 +103,18 @@ python tools/export_model.py -c configs/{MODEL.yml} --slim_config configs/slim/{
| 模型 | 压缩策略 | GFLOPs | 模型体积(MB) | 输入尺寸 | 预测时延(SD855) | Box AP | 下载 | 模型配置文件 | 压缩算法配置文件 |
| :---------: | :-------: | :------------: |:-------------: | :------: | :-------------: | :------: | :-----------------------------------------------------: |:-------------: | :------: |
| YOLOv3-MobileNetV1 | baseline | 24.13 | 93 | 608 | 332.0ms | 75.1 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) | - |
| YOLOv3-MobileNetV1 | 剪裁-l1_norm(sensity) | 15.78(-34.49%) | 66(-29%) | 608 | - | 78.4(+3.3) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_voc_prune_l1_norm.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/prune/yolov3_prune_l1_norm.yml) |
| YOLOv3-MobileNetV1 | baseline | 24.13 | 93 | 608 | 332.0ms | 75.1 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) | - |
| YOLOv3-MobileNetV1 | 剪裁-l1_norm(sensity) | 15.78(-34.49%) | 66(-29%) | 608 | - | 78.4(+3.3) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_voc_prune_l1_norm.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/prune/yolov3_prune_l1_norm.yml) |
#### COCO上benchmark
| 模型 | 压缩策略 | GFLOPs | 模型体积(MB) | 输入尺寸 | 预测时延(SD855) | Box AP | 下载 | 模型配置文件 | 压缩算法配置文件 |
| :---------: | :-------: | :------------: |:-------------: | :------: | :-------------: | :------: | :-----------------------------------------------------: |:-------------: | :------: |
| PP-YOLO-MobileNetV3_large | baseline | -- | 18.5 | 608 | 25.1ms | 23.2 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | - |
| PP-YOLO-MobileNetV3_large | 剪裁-FPGM | -37% | 12.6 | 608 | - | 22.3 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_prune_fpgm.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/prune/ppyolo_mbv3_large_prune_fpgm.yml) |
| YOLOv3-DarkNet53 | baseline | -- | 238.2 | 608 | - | 39.0 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) | - |
| YOLOv3-DarkNet53 | 剪裁-FPGM | -24% | - | 608 | - | 37.6 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_prune_fpgm.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/prune/yolov3_darknet_prune_fpgm.yml) |
| PP-YOLO_R50vd | baseline | -- | 183.3 | 608 | - | 44.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | - |
| PP-YOLO_R50vd | 剪裁-FPGM | -35% | - | 608 | - | 42.1 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_prune_fpgm.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/prune/ppyolo_r50vd_prune_fpgm.yml) |
| PP-YOLO-MobileNetV3_large | baseline | -- | 18.5 | 608 | 25.1ms | 23.2 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | - |
| PP-YOLO-MobileNetV3_large | 剪裁-FPGM | -37% | 12.6 | 608 | - | 22.3 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_prune_fpgm.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/prune/ppyolo_mbv3_large_prune_fpgm.yml) |
| YOLOv3-DarkNet53 | baseline | -- | 238.2 | 608 | - | 39.0 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) | - |
| YOLOv3-DarkNet53 | 剪裁-FPGM | -24% | - | 608 | - | 37.6 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_prune_fpgm.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/prune/yolov3_darknet_prune_fpgm.yml) |
| PP-YOLO_R50vd | baseline | -- | 183.3 | 608 | - | 44.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | - |
| PP-YOLO_R50vd | 剪裁-FPGM | -35% | - | 608 | - | 42.1 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_prune_fpgm.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/prune/ppyolo_r50vd_prune_fpgm.yml) |
说明:
- 目前剪裁除RCNN系列模型外,其余模型均已支持。
......@@ -126,22 +126,22 @@ python tools/export_model.py -c configs/{MODEL.yml} --slim_config configs/slim/{
| 模型 | 压缩策略 | 输入尺寸 | 模型体积(MB) | 预测时延(V100) | 预测时延(SD855) | Box AP | 下载 | Inference模型下载 | 模型配置文件 | 压缩算法配置文件 |
| ------------------ | ------------ | -------- | :---------: | :---------: |:---------: | :---------: | :----------------------------------------------: | :----------------------------------------------: |:------------------------------------------: | :------------------------------------: |
| PP-YOLOv2_R50vd | baseline | 640 | 208.6 | 19.1ms | -- | 49.1 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolov2_r50vd_dcn_365e_coco.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | - |
| PP-YOLOv2_R50vd | PACT在线量化 | 640 | -- | 17.3ms | -- | 48.1 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolov2_r50vd_dcn_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolov2_r50vd_dcn_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/ppyolov2_r50vd_dcn_qat.yml) |
| PP-YOLO_R50vd | baseline | 608 | 183.3 | 17.4ms | -- | 44.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_dcn_1x_coco.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | - |
| PP-YOLO_R50vd | PACT在线量化 | 608 | 67.3 | 13.8ms | -- | 44.3 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_qat_pact.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_qat_pact.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/ppyolo_r50vd_qat_pact.yml) |
| PP-YOLO-MobileNetV3_large | baseline | 320 | 18.5 | 2.7ms | 27.9ms | 23.2 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_coco.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | - |
| PP-YOLO-MobileNetV3_large | 普通在线量化 | 320 | 5.6 | -- | 25.1ms | 24.3 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/ppyolo_mbv3_large_qat.yml) |
| YOLOv3-MobileNetV1 | baseline | 608 | 94.2 | 8.9ms | 332ms | 29.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_270e_coco.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | - |
| YOLOv3-MobileNetV1 | 普通在线量化 | 608 | 25.4 | 6.6ms | 248ms | 30.5 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/yolov3_mobilenet_v1_qat.yml) |
| YOLOv3-MobileNetV3 | baseline | 608 | 90.3 | 9.4ms | 367.2ms | 31.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v3_large_270e_coco.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) | - |
| YOLOv3-MobileNetV3 | PACT在线量化 | 608 | 24.4 | 8.0ms | 280.0ms | 31.1 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v3_coco_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v3_coco_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/yolov3_mobilenet_v3_qat.yml) |
| YOLOv3-DarkNet53 | baseline | 608 | 238.2 | 16.0ms | -- | 39.0 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet53_270e_coco.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) | - |
| YOLOv3-DarkNet53 | 普通在线量化 | 608 | 78.8 | 12.4ms | -- | 38.8 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_coco_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_coco_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/yolov3_darknet_qat.yml) |
| SSD-MobileNet_v1 | baseline | 300 | 22.5 | 4.4ms | 26.6ms | 73.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ssd_mobilenet_v1_300_120e_voc.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ssd_mobilenet_v1_300_120e_voc.tar) |[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) | - |
| SSD-MobileNet_v1 | 普通在线量化 | 300 | 7.1 | -- | 21.5ms | 72.9 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ssd_mobilenet_v1_300_voc_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ssd_mobilenet_v1_300_voc_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/ssd_mobilenet_v1_qat.yml) |
| Mask-ResNet50-FPN | baseline | (800, 1333) | 174.1 | 359.5ms | -- | 39.2/35.6 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_1x_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/mask_rcnn_r50_fpn_1x_coco.tar) |[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.yml) | - |
| Mask-ResNet50-FPN | 普通在线量化 | (800, 1333) | -- | -- | -- | 39.7(+0.5)/35.9(+0.3) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/mask_rcnn_r50_fpn_1x_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/mask_rcnn_r50_fpn_1x_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/mask_rcnn_r50_fpn_1x_qat.yml) |
| PP-YOLOv2_R50vd | baseline | 640 | 208.6 | 19.1ms | -- | 49.1 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolov2_r50vd_dcn_365e_coco.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | - |
| PP-YOLOv2_R50vd | PACT在线量化 | 640 | -- | 17.3ms | -- | 48.1 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolov2_r50vd_dcn_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolov2_r50vd_dcn_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/ppyolov2_r50vd_dcn_qat.yml) |
| PP-YOLO_R50vd | baseline | 608 | 183.3 | 17.4ms | -- | 44.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_dcn_1x_coco.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | - |
| PP-YOLO_R50vd | PACT在线量化 | 608 | 67.3 | 13.8ms | -- | 44.3 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_qat_pact.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_qat_pact.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/ppyolo_r50vd_qat_pact.yml) |
| PP-YOLO-MobileNetV3_large | baseline | 320 | 18.5 | 2.7ms | 27.9ms | 23.2 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_coco.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | - |
| PP-YOLO-MobileNetV3_large | 普通在线量化 | 320 | 5.6 | -- | 25.1ms | 24.3 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/ppyolo_mbv3_large_qat.yml) |
| YOLOv3-MobileNetV1 | baseline | 608 | 94.2 | 8.9ms | 332ms | 29.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_270e_coco.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | - |
| YOLOv3-MobileNetV1 | 普通在线量化 | 608 | 25.4 | 6.6ms | 248ms | 30.5 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/yolov3_mobilenet_v1_qat.yml) |
| YOLOv3-MobileNetV3 | baseline | 608 | 90.3 | 9.4ms | 367.2ms | 31.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v3_large_270e_coco.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) | - |
| YOLOv3-MobileNetV3 | PACT在线量化 | 608 | 24.4 | 8.0ms | 280.0ms | 31.1 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v3_coco_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v3_coco_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/yolov3_mobilenet_v3_qat.yml) |
| YOLOv3-DarkNet53 | baseline | 608 | 238.2 | 16.0ms | -- | 39.0 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet53_270e_coco.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) | - |
| YOLOv3-DarkNet53 | 普通在线量化 | 608 | 78.8 | 12.4ms | -- | 38.8 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_coco_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_coco_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/yolov3_darknet_qat.yml) |
| SSD-MobileNet_v1 | baseline | 300 | 22.5 | 4.4ms | 26.6ms | 73.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ssd_mobilenet_v1_300_120e_voc.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ssd_mobilenet_v1_300_120e_voc.tar) |[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) | - |
| SSD-MobileNet_v1 | 普通在线量化 | 300 | 7.1 | -- | 21.5ms | 72.9 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ssd_mobilenet_v1_300_voc_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ssd_mobilenet_v1_300_voc_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/ssd_mobilenet_v1_qat.yml) |
| Mask-ResNet50-FPN | baseline | (800, 1333) | 174.1 | 359.5ms | -- | 39.2/35.6 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_1x_coco.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/mask_rcnn_r50_fpn_1x_coco.tar) |[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.yml) | - |
| Mask-ResNet50-FPN | 普通在线量化 | (800, 1333) | -- | -- | -- | 39.7(+0.5)/35.9(+0.3) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/mask_rcnn_r50_fpn_1x_qat.pdparams) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/mask_rcnn_r50_fpn_1x_qat.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/mask_rcnn_r50_fpn_1x_qat.yml) |
说明:
- 上述V100预测时延非量化模型均是使用TensorRT-FP32测试,量化模型均使用TensorRT-INT8测试,并且都包含NMS耗时。
......@@ -163,8 +163,8 @@ python3.7 tools/post_quant.py -c configs/ppyolo/ppyolo_mbv3_large_coco.yml --sli
| 模型 | 压缩策略 | 输入尺寸 | Box AP | 下载 | 模型配置文件 | 压缩算法配置文件 |
| ------------------ | ------------ | -------- | :---------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| YOLOv3-MobileNetV1 | baseline | 608 | 29.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | - |
| YOLOv3-MobileNetV1 | 蒸馏 | 608 | 31.0(+1.6) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_distill.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/distill/yolov3_mobilenet_v1_coco_distill.yml) |
| YOLOv3-MobileNetV1 | baseline | 608 | 29.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | - |
| YOLOv3-MobileNetV1 | 蒸馏 | 608 | 31.0(+1.6) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_distill.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/distill/yolov3_mobilenet_v1_coco_distill.yml) |
- 具体蒸馏方法请参考[蒸馏策略文档](distill/README.md)
......@@ -174,5 +174,5 @@ python3.7 tools/post_quant.py -c configs/ppyolo/ppyolo_mbv3_large_coco.yml --sli
| 模型 | 压缩策略 | 输入尺寸 | GFLOPs | 模型体积(MB) | 预测时延(SD855) | Box AP | 下载 | 模型配置文件 | 压缩算法配置文件 |
| ------------------ | ------------ | -------- | :---------: |:---------: |:---------: | :---------: |:----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| YOLOv3-MobileNetV1 | baseline | 608 | 24.65 | 94.2 | 332.0ms | 29.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | - |
| YOLOv3-MobileNetV1 | 蒸馏+剪裁 | 608 | 7.54(-69.4%) | 30.9(-67.2%) | 166.1ms | 28.4(-1.0) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_distill_prune.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/extensions/yolov3_mobilenet_v1_coco_distill_prune.yml) |
| YOLOv3-MobileNetV1 | baseline | 608 | 24.65 | 94.2 | 332.0ms | 29.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | - |
| YOLOv3-MobileNetV1 | 蒸馏+剪裁 | 608 | 7.54(-69.4%) | 30.9(-67.2%) | 166.1ms | 28.4(-1.0) | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_distill_prune.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/extensions/yolov3_mobilenet_v1_coco_distill_prune.yml) |
......@@ -101,18 +101,18 @@ python tools/export_model.py -c configs/{MODEL.yml} --slim_config configs/slim/{
| Model | Compression Strategy | GFLOPs | Model Volume(MB) | Input Size | Predict Delay(SD855) | Box AP | Download | Model Configuration File | Compression Algorithm Configuration File |
| :----------------: | :-------------------: | :------------: | :--------------: | :--------: | :------------------: | :--------: | :------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: |
| YOLOv3-MobileNetV1 | baseline | 24.13 | 93 | 608 | 332.0ms | 75.1 | [link](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) | - |
| YOLOv3-MobileNetV1 | 剪裁-l1_norm(sensity) | 15.78(-34.49%) | 66(-29%) | 608 | - | 78.4(+3.3) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_voc_prune_l1_norm.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) | [slim configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/prune/yolov3_prune_l1_norm.yml) |
| YOLOv3-MobileNetV1 | baseline | 24.13 | 93 | 608 | 332.0ms | 75.1 | [link](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) | - |
| YOLOv3-MobileNetV1 | 剪裁-l1_norm(sensity) | 15.78(-34.49%) | 66(-29%) | 608 | - | 78.4(+3.3) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_voc_prune_l1_norm.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) | [slim configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/prune/yolov3_prune_l1_norm.yml) |
#### COCO Benchmark
| Mode | Compression Strategy | GFLOPs | Model Volume(MB) | Input Size | Predict Delay(SD855) | Box AP | Download | Model Configuration File | Compression Algorithm Configuration File |
| :-----------------------: | :------------------: | :----: | :--------------: | :--------: | :------------------: | :----: | :---------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------: |
| PP-YOLO-MobileNetV3_large | baseline | -- | 18.5 | 608 | 25.1ms | 23.2 | [link](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | - |
| PP-YOLO-MobileNetV3_large | 剪裁-FPGM | -37% | 12.6 | 608 | - | 22.3 | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_prune_fpgm.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | [slim configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/prune/ppyolo_mbv3_large_prune_fpgm.yml) |
| YOLOv3-DarkNet53 | baseline | -- | 238.2 | 608 | - | 39.0 | [link](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) | - |
| YOLOv3-DarkNet53 | 剪裁-FPGM | -24% | - | 608 | - | 37.6 | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_prune_fpgm.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) | [slim configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/prune/yolov3_darknet_prune_fpgm.yml) |
| PP-YOLO_R50vd | baseline | -- | 183.3 | 608 | - | 44.8 | [link](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | - |
| PP-YOLO_R50vd | 剪裁-FPGM | -35% | - | 608 | - | 42.1 | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_prune_fpgm.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | [slim configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/prune/ppyolo_r50vd_prune_fpgm.yml) |
| PP-YOLO-MobileNetV3_large | baseline | -- | 18.5 | 608 | 25.1ms | 23.2 | [link](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | - |
| PP-YOLO-MobileNetV3_large | 剪裁-FPGM | -37% | 12.6 | 608 | - | 22.3 | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_prune_fpgm.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | [slim configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/prune/ppyolo_mbv3_large_prune_fpgm.yml) |
| YOLOv3-DarkNet53 | baseline | -- | 238.2 | 608 | - | 39.0 | [link](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) | - |
| YOLOv3-DarkNet53 | 剪裁-FPGM | -24% | - | 608 | - | 37.6 | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_prune_fpgm.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) | [slim configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/prune/yolov3_darknet_prune_fpgm.yml) |
| PP-YOLO_R50vd | baseline | -- | 183.3 | 608 | - | 44.8 | [link](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | - |
| PP-YOLO_R50vd | 剪裁-FPGM | -35% | - | 608 | - | 42.1 | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_prune_fpgm.pdparams) | [configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | [slim configuration file](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/prune/ppyolo_r50vd_prune_fpgm.yml) |
Description:
- Currently, all models except RCNN series models are supported.
......@@ -124,22 +124,22 @@ Description:
| Model | Compression Strategy | Input Size | Model Volume(MB) | Prediction Delay(V100) | Prediction Delay(SD855) | Box AP | Download | Download of Inference Model | Model Configuration File | Compression Algorithm Configuration File |
| ------------------------- | -------------------------- | ----------- | :--------------: | :--------------------: | :---------------------: | :-------------------: | :-----------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------: |
| PP-YOLOv2_R50vd | baseline | 640 | 208.6 | 19.1ms | -- | 49.1 | [link](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolov2_r50vd_dcn_365e_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | - |
| PP-YOLOv2_R50vd | PACT Online quantitative | 640 | -- | 17.3ms | -- | 48.1 | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolov2_r50vd_dcn_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolov2_r50vd_dcn_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/ppyolov2_r50vd_dcn_qat.yml) |
| PP-YOLO_R50vd | baseline | 608 | 183.3 | 17.4ms | -- | 44.8 | [link](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_dcn_1x_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | - |
| PP-YOLO_R50vd | PACT Online quantitative | 608 | 67.3 | 13.8ms | -- | 44.3 | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_qat_pact.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_qat_pact.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/ppyolo_r50vd_qat_pact.yml) |
| PP-YOLO-MobileNetV3_large | baseline | 320 | 18.5 | 2.7ms | 27.9ms | 23.2 | [link](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | - |
| PP-YOLO-MobileNetV3_large | Common Online quantitative | 320 | 5.6 | -- | 25.1ms | 24.3 | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/ppyolo_mbv3_large_qat.yml) |
| YOLOv3-MobileNetV1 | baseline | 608 | 94.2 | 8.9ms | 332ms | 29.4 | [link](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_270e_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | - |
| YOLOv3-MobileNetV1 | Common Online quantitative | 608 | 25.4 | 6.6ms | 248ms | 30.5 | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | [slim Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/yolov3_mobilenet_v1_qat.yml) |
| YOLOv3-MobileNetV3 | baseline | 608 | 90.3 | 9.4ms | 367.2ms | 31.4 | [link](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v3_large_270e_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) | - |
| YOLOv3-MobileNetV3 | PACT Online quantitative | 608 | 24.4 | 8.0ms | 280.0ms | 31.1 | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v3_coco_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v3_coco_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) | [slim Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/yolov3_mobilenet_v3_qat.yml) |
| YOLOv3-DarkNet53 | baseline | 608 | 238.2 | 16.0ms | -- | 39.0 | [link](https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet53_270e_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) | - |
| YOLOv3-DarkNet53 | Common Online quantitative | 608 | 78.8 | 12.4ms | -- | 38.8 | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_coco_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_coco_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) | [slim Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/yolov3_darknet_qat.yml) |
| SSD-MobileNet_v1 | baseline | 300 | 22.5 | 4.4ms | 26.6ms | 73.8 | [link](https://paddledet.bj.bcebos.com/models/ssd_mobilenet_v1_300_120e_voc.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ssd_mobilenet_v1_300_120e_voc.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) | - |
| SSD-MobileNet_v1 | Common Online quantitative | 300 | 7.1 | -- | 21.5ms | 72.9 | [link](https://paddledet.bj.bcebos.com/models/slim/ssd_mobilenet_v1_300_voc_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ssd_mobilenet_v1_300_voc_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) | [slim Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/ssd_mobilenet_v1_qat.yml) |
| Mask-ResNet50-FPN | baseline | (800, 1333) | 174.1 | 359.5ms | -- | 39.2/35.6 | [link](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_1x_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/mask_rcnn_r50_fpn_1x_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.yml) | - |
| Mask-ResNet50-FPN | Common Online quantitative | (800, 1333) | -- | -- | -- | 39.7(+0.5)/35.9(+0.3) | [link](https://paddledet.bj.bcebos.com/models/slim/mask_rcnn_r50_fpn_1x_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/mask_rcnn_r50_fpn_1x_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.yml) | [slim Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/quant/mask_rcnn_r50_fpn_1x_qat.yml) |
| PP-YOLOv2_R50vd | baseline | 640 | 208.6 | 19.1ms | -- | 49.1 | [link](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolov2_r50vd_dcn_365e_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | - |
| PP-YOLOv2_R50vd | PACT Online quantitative | 640 | -- | 17.3ms | -- | 48.1 | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolov2_r50vd_dcn_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolov2_r50vd_dcn_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/ppyolov2_r50vd_dcn_qat.yml) |
| PP-YOLO_R50vd | baseline | 608 | 183.3 | 17.4ms | -- | 44.8 | [link](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_dcn_1x_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | - |
| PP-YOLO_R50vd | PACT Online quantitative | 608 | 67.3 | 13.8ms | -- | 44.3 | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_qat_pact.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_r50vd_qat_pact.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/ppyolo_r50vd_qat_pact.yml) |
| PP-YOLO-MobileNetV3_large | baseline | 320 | 18.5 | 2.7ms | 27.9ms | 23.2 | [link](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | - |
| PP-YOLO-MobileNetV3_large | Common Online quantitative | 320 | 5.6 | -- | 25.1ms | 24.3 | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo/ppyolo_mbv3_large_coco.yml) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/ppyolo_mbv3_large_qat.yml) |
| YOLOv3-MobileNetV1 | baseline | 608 | 94.2 | 8.9ms | 332ms | 29.4 | [link](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_270e_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | - |
| YOLOv3-MobileNetV1 | Common Online quantitative | 608 | 25.4 | 6.6ms | 248ms | 30.5 | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | [slim Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/yolov3_mobilenet_v1_qat.yml) |
| YOLOv3-MobileNetV3 | baseline | 608 | 90.3 | 9.4ms | 367.2ms | 31.4 | [link](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v3_large_270e_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) | - |
| YOLOv3-MobileNetV3 | PACT Online quantitative | 608 | 24.4 | 8.0ms | 280.0ms | 31.1 | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v3_coco_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v3_coco_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) | [slim Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/yolov3_mobilenet_v3_qat.yml) |
| YOLOv3-DarkNet53 | baseline | 608 | 238.2 | 16.0ms | -- | 39.0 | [link](https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet53_270e_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) | - |
| YOLOv3-DarkNet53 | Common Online quantitative | 608 | 78.8 | 12.4ms | -- | 38.8 | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_coco_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_coco_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) | [slim Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/yolov3_darknet_qat.yml) |
| SSD-MobileNet_v1 | baseline | 300 | 22.5 | 4.4ms | 26.6ms | 73.8 | [link](https://paddledet.bj.bcebos.com/models/ssd_mobilenet_v1_300_120e_voc.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ssd_mobilenet_v1_300_120e_voc.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) | - |
| SSD-MobileNet_v1 | Common Online quantitative | 300 | 7.1 | -- | 21.5ms | 72.9 | [link](https://paddledet.bj.bcebos.com/models/slim/ssd_mobilenet_v1_300_voc_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/ssd_mobilenet_v1_300_voc_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) | [slim Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/ssd_mobilenet_v1_qat.yml) |
| Mask-ResNet50-FPN | baseline | (800, 1333) | 174.1 | 359.5ms | -- | 39.2/35.6 | [link](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_1x_coco.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/mask_rcnn_r50_fpn_1x_coco.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.yml) | - |
| Mask-ResNet50-FPN | Common Online quantitative | (800, 1333) | -- | -- | -- | 39.7(+0.5)/35.9(+0.3) | [link](https://paddledet.bj.bcebos.com/models/slim/mask_rcnn_r50_fpn_1x_qat.pdparams) | [link](https://paddledet.bj.bcebos.com/models/slim/mask_rcnn_r50_fpn_1x_qat.tar) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.yml) | [slim Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/quant/mask_rcnn_r50_fpn_1x_qat.yml) |
Description:
- The above V100 prediction delay non-quantified model is tested by TensorRT FP32, and the quantified model is tested by TensorRT INT8, and both of them include NMS time.
......@@ -151,8 +151,8 @@ Description:
| Model | Compression Strategy | Input Size | Box AP | Download | Model Configuration File | Compression Strategy Configuration File |
| ------------------ | -------------------- | ---------- | :--------: | :-------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------: |
| YOLOv3-MobileNetV1 | baseline | 608 | 29.4 | [link](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | - |
| YOLOv3-MobileNetV1 | Distillation | 608 | 31.0(+1.6) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_distill.pdparams) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | [slimConfiguration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/distill/yolov3_mobilenet_v1_coco_distill.yml) |
| YOLOv3-MobileNetV1 | baseline | 608 | 29.4 | [link](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | - |
| YOLOv3-MobileNetV1 | Distillation | 608 | 31.0(+1.6) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_distill.pdparams) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | [slimConfiguration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/distill/yolov3_mobilenet_v1_coco_distill.yml) |
- Please refer to the specific distillation method[Distillation Policy Document](distill/README.md)
......@@ -162,5 +162,5 @@ Description:
| Model | Compression Strategy | Input Size | GFLOPs | Model Volume(MB) | Prediction Delay(SD855) | Box AP | Download | Model Configuration File | Compression Algorithm Configuration File |
| ------------------ | ------------------------ | ---------- | :----------: | :--------------: | :---------------------: | :--------: | :-------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: |
| YOLOv3-MobileNetV1 | baseline | 608 | 24.65 | 94.2 | 332.0ms | 29.4 | [link](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | - |
| YOLOv3-MobileNetV1 | Distillation + Tailoring | 608 | 7.54(-69.4%) | 30.9(-67.2%) | 166.1ms | 28.4(-1.0) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_distill_prune.pdparams) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | [slimConfiguration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/extensions/yolov3_mobilenet_v1_coco_distill_prune.yml) |
| YOLOv3-MobileNetV1 | baseline | 608 | 24.65 | 94.2 | 332.0ms | 29.4 | [link](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | - |
| YOLOv3-MobileNetV1 | Distillation + Tailoring | 608 | 7.54(-69.4%) | 30.9(-67.2%) | 166.1ms | 28.4(-1.0) | [link](https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_coco_distill_prune.pdparams) | [Configuration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) | [slimConfiguration File ](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/slim/extensions/yolov3_mobilenet_v1_coco_distill_prune.yml) |
......@@ -3,7 +3,7 @@
## YOLOv3模型蒸馏
以YOLOv3-MobileNetV1为例,使用YOLOv3-ResNet34作为蒸馏训练的teacher网络, 对YOLOv3-MobileNetV1结构的student网络进行蒸馏。
COCO数据集作为目标检测任务的训练目标难度更大,意味着teacher网络会预测出更多的背景bbox,如果直接用teacher的预测输出作为student学习的`soft label`会有严重的类别不均衡问题。解决这个问题需要引入新的方法,详细背景请参考论文:[Object detection at 200 Frames Per Second](https://arxiv.org/abs/1805.06361)
为了确定蒸馏的对象,我们首先需要找到student和teacher网络得到的`x,y,w,h,cls,objness`等Tensor,用teacher得到的结果指导student训练。具体实现可参考[代码](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/ppdet/slim/distill.py)
为了确定蒸馏的对象,我们首先需要找到student和teacher网络得到的`x,y,w,h,cls,objness`等Tensor,用teacher得到的结果指导student训练。具体实现可参考[代码](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/ppdet/slim/distill.py)
## Citations
```
......
......@@ -19,9 +19,9 @@ SOLOv2 (Segmenting Objects by Locations) is a fast instance segmentation framewo
| BlendMask | R50-FPN | True | 3x | 37.8 | 13.5 | V100 | - | - |
| SOLOv2 (Paper) | R50-FPN | False | 1x | 34.8 | 18.5 | V100 | - | - |
| SOLOv2 (Paper) | X101-DCN-FPN | True | 3x | 42.4 | 5.9 | V100 | - | - |
| SOLOv2 | R50-FPN | False | 1x | 35.5 | 21.9 | V100 | [model](https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/solov2/solov2_r50_fpn_1x_coco.yml) |
| SOLOv2 | R50-FPN | True | 3x | 38.0 | 21.9 | V100 | [model](https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_3x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/solov2/solov2_r50_fpn_3x_coco.yml) |
| SOLOv2 | R101vd-FPN | True | 3x | 42.7 | 12.1 | V100 | [model](https://paddledet.bj.bcebos.com/models/solov2_r101_vd_fpn_3x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/solov2/solov2_r101_vd_fpn_3x_coco.yml) |
| SOLOv2 | R50-FPN | False | 1x | 35.5 | 21.9 | V100 | [model](https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/solov2/solov2_r50_fpn_1x_coco.yml) |
| SOLOv2 | R50-FPN | True | 3x | 38.0 | 21.9 | V100 | [model](https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_3x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/solov2/solov2_r50_fpn_3x_coco.yml) |
| SOLOv2 | R101vd-FPN | True | 3x | 42.7 | 12.1 | V100 | [model](https://paddledet.bj.bcebos.com/models/solov2_r101_vd_fpn_3x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/solov2/solov2_r101_vd_fpn_3x_coco.yml) |
**Notes:**
......@@ -30,7 +30,7 @@ SOLOv2 (Segmenting Objects by Locations) is a fast instance segmentation framewo
## Enhanced model
| Backbone | Input size | Lr schd | V100 FP32(FPS) | Mask AP<sup>val</sup> | Download | Configs |
| :---------------------: | :-------------------: | :-----: | :------------: | :-----: | :---------: | :------------------------: |
| Light-R50-VD-DCN-FPN | 512 | 3x | 38.6 | 39.0 | [model](https://paddledet.bj.bcebos.com/models/solov2_r50_enhance_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/solov2/solov2_r50_enhance_coco.yml) |
| Light-R50-VD-DCN-FPN | 512 | 3x | 38.6 | 39.0 | [model](https://paddledet.bj.bcebos.com/models/solov2_r50_enhance_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/solov2/solov2_r50_enhance_coco.yml) |
**Optimizing method of enhanced model:**
- Better backbone network: ResNet50vd-DCN
......
......@@ -6,8 +6,8 @@
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 |
| :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
| VGG | SSD | 8 | 240e | ---- | 77.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ssd_vgg16_300_240e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ssd/ssd_vgg16_300_240e_voc.yml) |
| MobileNet v1 | SSD | 32 | 120e | ---- | 73.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ssd_mobilenet_v1_300_120e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) |
| VGG | SSD | 8 | 240e | ---- | 77.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ssd_vgg16_300_240e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ssd/ssd_vgg16_300_240e_voc.yml) |
| MobileNet v1 | SSD | 32 | 120e | ---- | 73.8 | [下载链接](https://paddledet.bj.bcebos.com/models/ssd_mobilenet_v1_300_120e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) |
**注意:** SSD-VGG使用4GPU在总batch size为32下训练240个epoch。SSD-MobileNetv1使用2GPU在总batch size为64下训练120周期。
......
......@@ -11,7 +11,7 @@ TOOD is an object detection model. We reproduced the model of the paper.
| Backbone | Model | Images/GPU | Inf time (fps) | Box AP | Config | Download |
|:------:|:--------:|:--------:|:--------------:|:------:|:------:|:--------:|
| R-50 | TOOD | 4 | --- | 42.5 | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/tood/tood_r50_fpn_1x_coco.yml) | [model](https://paddledet.bj.bcebos.com/models/tood_r50_fpn_1x_coco.pdparams) |
| R-50 | TOOD | 4 | --- | 42.5 | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/tood/tood_r50_fpn_1x_coco.yml) | [model](https://paddledet.bj.bcebos.com/models/tood_r50_fpn_1x_coco.pdparams) |
**Notes:**
......
......@@ -13,7 +13,7 @@ TTFNet是一种用于实时目标检测且对训练时间友好的网络,对Ce
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 |
| :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
| DarkNet53 | TTFNet | 12 | 1x | ---- | 33.5 | [下载链接](https://paddledet.bj.bcebos.com/models/ttfnet_darknet53_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ttfnet/ttfnet_darknet53_1x_coco.yml) |
| DarkNet53 | TTFNet | 12 | 1x | ---- | 33.5 | [下载链接](https://paddledet.bj.bcebos.com/models/ttfnet_darknet53_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ttfnet/ttfnet_darknet53_1x_coco.yml) |
......@@ -40,7 +40,7 @@ PAFNet系列模型从如下方面优化TTFNet模型:
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 |
| :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
| ResNet50vd | PAFNet | 18 | 10x | ---- | 39.8 | [下载链接](https://paddledet.bj.bcebos.com/models/pafnet_10x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ttfnet/pafnet_10x_coco.yml) |
| ResNet50vd | PAFNet | 18 | 10x | ---- | 39.8 | [下载链接](https://paddledet.bj.bcebos.com/models/pafnet_10x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ttfnet/pafnet_10x_coco.yml) |
......@@ -48,7 +48,7 @@ PAFNet系列模型从如下方面优化TTFNet模型:
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 | Box AP | 麒麟990延时(ms) | 体积(M) | 下载 | 配置文件 |
| :-------------- | :------------- | :-----: | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
| MobileNetv3 | PAFNet-Lite | 12 | 20x | 23.9 | 26.00 | 14 | [下载链接](https://paddledet.bj.bcebos.com/models/pafnet_lite_mobilenet_v3_20x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ttfnet/pafnet_lite_mobilenet_v3_20x_coco.yml) |
| MobileNetv3 | PAFNet-Lite | 12 | 20x | 23.9 | 26.00 | 14 | [下载链接](https://paddledet.bj.bcebos.com/models/pafnet_lite_mobilenet_v3_20x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ttfnet/pafnet_lite_mobilenet_v3_20x_coco.yml) |
**注意:** 由于动态图框架整体升级,PAFNet的PaddleDetection发布的权重模型评估时需要添加--bias字段, 例如
......
......@@ -14,7 +14,7 @@ The training time is short. Based on DarkNet53 backbone network, V100 8 cards on
| Backbone | Network type | Number of images per GPU | Learning rate strategy | Inferring time(fps) | Box AP | Download | Configuration File |
| :-------- | :----------- | :----------------------: | :--------------------: | :-----------------: | :----: | :------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------: |
| DarkNet53 | TTFNet | 12 | 1x | ---- | 33.5 | [link](https://paddledet.bj.bcebos.com/models/ttfnet_darknet53_1x_coco.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ttfnet/ttfnet_darknet53_1x_coco.yml) |
| DarkNet53 | TTFNet | 12 | 1x | ---- | 33.5 | [link](https://paddledet.bj.bcebos.com/models/ttfnet_darknet53_1x_coco.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ttfnet/ttfnet_darknet53_1x_coco.yml) |
......@@ -41,7 +41,7 @@ PAFNet series models optimize TTFNet model from the following aspects:
| Backbone | Net type | Number of images per GPU | Learning rate strategy | Inferring time(fps) | Box AP | Download | Configuration File |
| :--------- | :------- | :----------------------: | :--------------------: | :-----------------: | :----: | :---------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: |
| ResNet50vd | PAFNet | 18 | 10x | ---- | 39.8 | [link](https://paddledet.bj.bcebos.com/models/pafnet_10x_coco.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ttfnet/pafnet_10x_coco.yml) |
| ResNet50vd | PAFNet | 18 | 10x | ---- | 39.8 | [link](https://paddledet.bj.bcebos.com/models/pafnet_10x_coco.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ttfnet/pafnet_10x_coco.yml) |
......@@ -49,7 +49,7 @@ PAFNet series models optimize TTFNet model from the following aspects:
| Backbone | Net type | Number of images per GPU | Learning rate strategy | Box AP | kirin 990 delay(ms) | volume(M) | Download | Configuration File |
| :---------- | :---------- | :----------------------: | :--------------------: | :----: | :-------------------: | :---------: | :---------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------: |
| MobileNetv3 | PAFNet-Lite | 12 | 20x | 23.9 | 26.00 | 14 | [link](https://paddledet.bj.bcebos.com/models/pafnet_lite_mobilenet_v3_20x_coco.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ttfnet/pafnet_lite_mobilenet_v3_20x_coco.yml) |
| MobileNetv3 | PAFNet-Lite | 12 | 20x | 23.9 | 26.00 | 14 | [link](https://paddledet.bj.bcebos.com/models/pafnet_lite_mobilenet_v3_20x_coco.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ttfnet/pafnet_lite_mobilenet_v3_20x_coco.yml) |
**Attention:** Due to the overall upgrade of the dynamic graph framework, the weighting model published by PaddleDetection of PAF Net needs to be evaluated with a --bias field, for example
......
......@@ -5,7 +5,7 @@ We provide some models implemented by PaddlePaddle to detect objects in specific
| Task | Algorithm | Box AP | Download | Configs |
|:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: |:------:|
| Vehicle Detection | YOLOv3 | 54.5 | [model](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/vehicle/vehicle_yolov3_darknet.yml) |
| Vehicle Detection | YOLOv3 | 54.5 | [model](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/vehicle/vehicle_yolov3_darknet.yml) |
## Vehicle Detection
......@@ -17,7 +17,7 @@ The network for detecting vehicles is YOLOv3, the backbone of which is Dacknet53
### 2. Configuration for training
PaddleDetection provides users with a configuration file [yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for vehicle detection:
PaddleDetection provides users with a configuration file [yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for vehicle detection:
* num_classes: 6
* anchors: [[8, 9], [10, 23], [19, 15], [23, 33], [40, 25], [54, 50], [101, 80], [139, 145], [253, 224]]
......
......@@ -5,7 +5,7 @@
| 任务 | 算法 | 精度(Box AP) | 下载 | 配置文件 |
|:---------------------|:---------:|:------:| :---------------------------------------------------------------------------------: | :------:|
| 车辆检测 | YOLOv3 | 54.5 | [下载链接](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/vehicle/vehicle_yolov3_darknet.yml) |
| 车辆检测 | YOLOv3 | 54.5 | [下载链接](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/vehicle/vehicle_yolov3_darknet.yml) |
## 车辆检测(Vehicle Detection)
......@@ -18,7 +18,7 @@ Backbone为Dacknet53的YOLOv3。
### 2. 训练参数配置
PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml),与之相比,在进行车辆检测的模型训练时,我们对以下参数进行了修改:
PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml),与之相比,在进行车辆检测的模型训练时,我们对以下参数进行了修改:
* num_classes: 6
* anchors: [[8, 9], [10, 23], [19, 15], [23, 33], [40, 25], [54, 50], [101, 80], [139, 145], [253, 224]]
......
......@@ -9,41 +9,41 @@
| DarkNet53(paper) | 608 | 8 | 270e | ---- | 33.0 | - | - |
| DarkNet53(paper) | 416 | 8 | 270e | ---- | 31.0 | - | - |
| DarkNet53(paper) | 320 | 8 | 270e | ---- | 28.2 | - | - |
| DarkNet53 | 608 | 8 | 270e | ---- | 39.0 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) |
| DarkNet53 | 416 | 8 | 270e | ---- | 37.5 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) |
| DarkNet53 | 320 | 8 | 270e | ---- | 34.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) |
| ResNet50_vd | 608 | 8 | 270e | ---- | 39.1 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_r50vd_dcn_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml) |
| ResNet50_vd | 416 | 8 | 270e | ---- | 36.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_r50vd_dcn_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml) |
| ResNet50_vd | 320 | 8 | 270e | ---- | 33.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_r50vd_dcn_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml) |
| ResNet34 | 608 | 8 | 270e | ---- | 36.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_r34_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_r34_270e_coco.yml) |
| ResNet34 | 416 | 8 | 270e | ---- | 34.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_r34_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_r34_270e_coco.yml) |
| ResNet34 | 320 | 8 | 270e | ---- | 31.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_r34_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_r34_270e_coco.yml) |
| MobileNet-V1 | 608 | 8 | 270e | ---- | 29.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) |
| MobileNet-V1 | 416 | 8 | 270e | ---- | 29.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) |
| MobileNet-V1 | 320 | 8 | 270e | ---- | 27.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) |
| MobileNet-V3 | 608 | 8 | 270e | ---- | 31.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) |
| MobileNet-V3 | 416 | 8 | 270e | ---- | 29.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) |
| MobileNet-V3 | 320 | 8 | 270e | ---- | 27.1 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) |
| MobileNet-V1-SSLD | 608 | 8 | 270e | ---- | 31.0 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml) |
| MobileNet-V1-SSLD | 416 | 8 | 270e | ---- | 30.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml) |
| MobileNet-V1-SSLD | 320 | 8 | 270e | ---- | 28.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml) |
| DarkNet53 | 608 | 8 | 270e | ---- | 39.0 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) |
| DarkNet53 | 416 | 8 | 270e | ---- | 37.5 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) |
| DarkNet53 | 320 | 8 | 270e | ---- | 34.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_darknet53_270e_coco.yml) |
| ResNet50_vd | 608 | 8 | 270e | ---- | 39.1 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_r50vd_dcn_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml) |
| ResNet50_vd | 416 | 8 | 270e | ---- | 36.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_r50vd_dcn_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml) |
| ResNet50_vd | 320 | 8 | 270e | ---- | 33.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_r50vd_dcn_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml) |
| ResNet34 | 608 | 8 | 270e | ---- | 36.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_r34_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_r34_270e_coco.yml) |
| ResNet34 | 416 | 8 | 270e | ---- | 34.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_r34_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_r34_270e_coco.yml) |
| ResNet34 | 320 | 8 | 270e | ---- | 31.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_r34_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_r34_270e_coco.yml) |
| MobileNet-V1 | 608 | 8 | 270e | ---- | 29.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) |
| MobileNet-V1 | 416 | 8 | 270e | ---- | 29.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) |
| MobileNet-V1 | 320 | 8 | 270e | ---- | 27.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) |
| MobileNet-V3 | 608 | 8 | 270e | ---- | 31.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) |
| MobileNet-V3 | 416 | 8 | 270e | ---- | 29.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) |
| MobileNet-V3 | 320 | 8 | 270e | ---- | 27.1 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml) |
| MobileNet-V1-SSLD | 608 | 8 | 270e | ---- | 31.0 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml) |
| MobileNet-V1-SSLD | 416 | 8 | 270e | ---- | 30.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml) |
| MobileNet-V1-SSLD | 320 | 8 | 270e | ---- | 28.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml) |
### YOLOv3 on Pasacl VOC
| 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | 下载 | 配置文件 |
| :----------- | :--: | :-----: | :-----: |:------------: |:----: | :-------: | :----: |
| MobileNet-V1 | 608 | 8 | 270e | - | 75.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) |
| MobileNet-V1 | 416 | 8 | 270e | - | 76.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) |
| MobileNet-V1 | 320 | 8 | 270e | - | 74.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) |
| MobileNet-V3 | 608 | 8 | 270e | - | 79.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml) |
| MobileNet-V3 | 416 | 8 | 270e | - | 78.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml) |
| MobileNet-V3 | 320 | 8 | 270e | - | 76.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml) |
| MobileNet-V1-SSLD | 608 | 8 | 270e | - | 78.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml) |
| MobileNet-V1-SSLD | 416 | 8 | 270e | - | 79.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml) |
| MobileNet-V1-SSLD | 320 | 8 | 270e | - | 77.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml) |
| MobileNet-V3-SSLD | 608 | 8 | 270e | - | 80.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml) |
| MobileNet-V3-SSLD | 416 | 8 | 270e | - | 79.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml) |
| MobileNet-V3-SSLD | 320 | 8 | 270e | - | 77.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml) |
| MobileNet-V1 | 608 | 8 | 270e | - | 75.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) |
| MobileNet-V1 | 416 | 8 | 270e | - | 76.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) |
| MobileNet-V1 | 320 | 8 | 270e | - | 74.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml) |
| MobileNet-V3 | 608 | 8 | 270e | - | 79.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml) |
| MobileNet-V3 | 416 | 8 | 270e | - | 78.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml) |
| MobileNet-V3 | 320 | 8 | 270e | - | 76.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml) |
| MobileNet-V1-SSLD | 608 | 8 | 270e | - | 78.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml) |
| MobileNet-V1-SSLD | 416 | 8 | 270e | - | 79.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml) |
| MobileNet-V1-SSLD | 320 | 8 | 270e | - | 77.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml) |
| MobileNet-V3-SSLD | 608 | 8 | 270e | - | 80.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml) |
| MobileNet-V3-SSLD | 416 | 8 | 270e | - | 79.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml) |
| MobileNet-V3-SSLD | 320 | 8 | 270e | - | 77.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml) |
**注意:** YOLOv3均使用8GPU训练,训练270个epoch。由于动态图框架整体升级,以下几个PaddleDetection发布的权重模型评估时需要添加--bias字段, 例如
......
......@@ -23,7 +23,6 @@
- 动作识别支持ST-GCN摔倒检测
- 框架功能优化:
- 支持混合精度训练,通过`–amp`开启
- EMA训练速度优化20%,优化EMA训练模型保存方式
- 支持infer预测结果保存为COCO格式
......
......@@ -23,7 +23,6 @@ English | [简体中文](./CHANGELOG.md)
- Release ST-GCN model for falldown action recognition
- Function Optimize:
- Support AMP training, enable with `--amp`
- Optimize 20% training speed when training with EMA, improve saving method of EMA weights
- Support saving inference results in COCO format
......
......@@ -22,7 +22,8 @@ Dependency of PaddleDetection and PaddlePaddle:
| PaddleDetection version | PaddlePaddle version | tips |
| :----------------: | :---------------: | :-------: |
| develop | >= 2.2.0rc | Dygraph mode is set as default |
| develop | >= 2.2.2 | Dygraph mode is set as default |
| release/2.4 | >= 2.2.2 | Dygraph mode is set as default |
| release/2.3 | >= 2.2.0rc | Dygraph mode is set as default |
| release/2.2 | >= 2.1.2 | Dygraph mode is set as default |
| release/2.1 | >= 2.1.0 | Dygraph mode is set as default |
......
......@@ -18,8 +18,9 @@ PaddleDetection 依赖 PaddlePaddle 版本关系:
| PaddleDetection版本 | PaddlePaddle版本 | 备注 |
| :------------------: | :---------------: | :-------: |
| develop | >= 2.2.0rc | 默认使用动态图模式 |
| release/2.3 | >= 2.2.0rc | 默认使用动态图模式 |
| develop | >= 2.2.2 | 默认使用动态图模式 |
| release/2.4 | >= 2.2.2 | 默认使用动态图模式 |
| release/2.3 | >= 2.2.0rc | 默认使用动态图模式 |
| release/2.2 | >= 2.1.2 | 默认使用动态图模式 |
| release/2.1 | >= 2.1.0 | 默认使用动态图模式 |
| release/2.0 | >= 2.0.1 | 默认使用动态图模式 |
......
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册