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未验证 提交 521a4a6a 编写于 作者: G Guanghua Yu 提交者： GitHub
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add config link in model zoo (#529) 

* add config link in model zoo

* fix link error

* add anchor free
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configs/anchor_free/README.md
浏览文件 @ 521a4a6a
...@@ -22,15 +22,15 @@ ...@@ -22,15 +22,15 @@
#### COCO数据集上的mAP #### COCO数据集上的mAP
| 网络结构 | 骨干网络 | 图片个数/GPU | 预训练模型 | mAP | FPS | 模型下载 | | 网络结构 | 骨干网络 | 图片个数/GPU | 预训练模型 | mAP | FPS | 模型下载 | 配置文件 |
|:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:| |:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:----------:|
| CornerNet-Squeeze | Hourglass104 | 14 | 无 | 34.5 | 35.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_hg104.tar) | | CornerNet-Squeeze | Hourglass104 | 14 | 无 | 34.5 | 35.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_hg104.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/cornernet_squeeze_hg104.yml) |
| CornerNet-Squeeze | ResNet50-vd | 14 | [faster\_rcnn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_2x.tar) | 32.7 | 42.45 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_r50_vd_fpn.tar) | | CornerNet-Squeeze | ResNet50-vd | 14 | [faster\_rcnn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_2x.tar) | 32.7 | 42.45 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_r50_vd_fpn.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/cornernet_squeeze_r50_vd_fpn.yml) |
| CornerNet-Squeeze-dcn | ResNet50-vd | 14 | [faster\_rcnn\_dcn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_2x.tar) | 34.9 | 40.05 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_dcn_r50_vd_fpn.tar) | | CornerNet-Squeeze-dcn | ResNet50-vd | 14 | [faster\_rcnn\_dcn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_2x.tar) | 34.9 | 40.05 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_dcn_r50_vd_fpn.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/cornernet_squeeze_dcn_r50_vd_fpn.yml) |
| CornerNet-Squeeze-dcn-mixup-cosine* | ResNet50-vd | 14 | [faster\_rcnn\_dcn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_2x.tar) | 38.2 | 40.05 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_dcn_r50_vd_fpn_mixup_cosine.pdparams) | | CornerNet-Squeeze-dcn-mixup-cosine* | ResNet50-vd | 14 | [faster\_rcnn\_dcn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_2x.tar) | 38.2 | 40.05 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_dcn_r50_vd_fpn_mixup_cosine.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/cornernet_squeeze_dcn_r50_vd_fpn_mixup_cosine.yml) |
| FCOS | ResNet50 | 2 | [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar) | 39.8 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_r50_fpn_1x.pdparams) | | FCOS | ResNet50 | 2 | [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar) | 39.8 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_r50_fpn_1x.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/fcos_r50_fpn_1x.yml) |
| FCOS+multiscale_train | ResNet50 | 2 | [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar) | 42.0 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_r50_fpn_multiscale_2x.pdparams) | | FCOS+multiscale_train | ResNet50 | 2 | [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar) | 42.0 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_r50_fpn_multiscale_2x.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/fcos_r50_fpn_multiscale_2x.yml) |
| FCOS+DCN | ResNet50 | 2 | [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar) | 44.4 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_dcn_r50_fpn_1x.pdparams) | | FCOS+DCN | ResNet50 | 2 | [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar) | 44.4 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_dcn_r50_fpn_1x.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/fcos_dcn_r50_fpn_1x.yml) |
**注意:** **注意:**
......
configs/anchor_free/cornernet_squeeze.yml → configs/anchor_free/cornernet_squeeze_hg104.yml
浏览文件 @ 521a4a6a
...@@ -7,7 +7,7 @@ save_dir: output ...@@ -7,7 +7,7 @@ save_dir: output
snapshot_iter: 10000 snapshot_iter: 10000
metric: COCO metric: COCO
pretrain_weights: NULL pretrain_weights: NULL
weights: output/cornernet_squeeze/model_final weights: output/cornernet_squeeze_hg104/model_final
num_classes: 80 num_classes: 80
stack: 2 stack: 2
......
configs/autoaugment/README.md
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...@@ -17,7 +17,7 @@ ...@@ -17,7 +17,7 @@
## Model Zoo ## Model Zoo
| Backbone | Type | AutoAug policy | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | | Backbone | Type | AutoAug policy | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | Configs |
| :---------------------- | :-------------:| :-------: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | | :---------------------- | :-------------:| :-------: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
| ResNet50-vd-FPN | Faster | v1 | 2 | 3x | 22.800 | 39.9 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_aa_3x.tar) | | ResNet50-vd-FPN | Faster | v1 | 2 | 3x | 22.800 | 39.9 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_aa_3x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/autoaugment/faster_rcnn_r50_vd_fpn_aa_3x.yml) |
| ResNet101-vd-FPN | Faster | v1 | 2 | 3x | 17.652 | 42.5 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_aa_3x.tar) | | ResNet101-vd-FPN | Faster | v1 | 2 | 3x | 17.652 | 42.5 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_aa_3x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/autoaugment/faster_rcnn_r101_vd_fpn_aa_3x.yml) |
configs/gcnet/README.md
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...@@ -28,7 +28,7 @@ ...@@ -28,7 +28,7 @@
## Model Zoo ## Model Zoo
| Backbone | Type | Context| Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | | Backbone | Type | Context| Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | Configs |
| :---------------------- | :-------------: | :-------------: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | | :---------------------- | :-------------: | :-------------: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
| ResNet50-vd-FPN | Mask | GC(c3-c5, r16, add) | 2 | 2x | 15.31 | 41.4 | 36.8 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_gcb_add_r16_2x.tar) | | ResNet50-vd-FPN | Mask | GC(c3-c5, r16, add) | 2 | 2x | 15.31 | 41.4 | 36.8 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_gcb_add_r16_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/gcnet/mask_rcnn_r50_vd_fpn_gcb_add_r16_2x.yml) |
| ResNet50-vd-FPN | Mask | GC(c3-c5, r16, mul) | 2 | 2x | 15.35 | 40.7 | 36.1 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_gcb_mul_r16_2x.tar) | | ResNet50-vd-FPN | Mask | GC(c3-c5, r16, mul) | 2 | 2x | 15.35 | 40.7 | 36.1 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_gcb_mul_r16_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/gcnet/mask_rcnn_r50_vd_fpn_gcb_mul_r16_2x.yml) |
configs/hrnet/README.md
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...@@ -28,7 +28,7 @@ ...@@ -28,7 +28,7 @@
## Model Zoo ## Model Zoo
| Backbone | Type | deformable Conv | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | | Backbone | Type | deformable Conv | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | Configs |
| :---------------------- | :------------- | :---: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | | :---------------------- | :------------- | :---: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
| HRNetV2p_W18 | Faster | False | 2 | 1x | 17.509 | 36.0 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_hrnetv2p_w18_1x.tar) | | HRNetV2p_W18 | Faster | False | 2 | 1x | 17.509 | 36.0 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_hrnetv2p_w18_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.yml) |
| HRNetV2p_W18 | Faster | False | 2 | 2x | 17.509 | 38.0 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_hrnetv2p_w18_2x.tar) | | HRNetV2p_W18 | Faster | False | 2 | 2x | 17.509 | 38.0 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_hrnetv2p_w18_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x.yml) |
configs/iou_loss/README.md
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...@@ -41,8 +41,8 @@ ...@@ -41,8 +41,8 @@
## Model Zoo ## Model Zoo
| Backbone | Type | Loss Type | Loss Weight | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | | Backbone | Type | Loss Type | Loss Weight | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | Configs |
| :---------------------- | :------------- | :---: | :---: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | | :---------------------- | :------------- | :---: | :---: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :---: |
| ResNet50-vd-FPN | Faster | GIOU | 10 | 2 | 1x | 22.94 | 39.4 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_giou_loss_1x.tar) | | ResNet50-vd-FPN | Faster | GIOU | 10 | 2 | 1x | 22.94 | 39.4 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_giou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/iou_loss/faster_rcnn_r50_vd_fpn_giou_loss_1x.yml) |
| ResNet50-vd-FPN | Faster | DIOU | 12 | 2 | 1x | 22.94 | 39.2 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_diou_loss_1x.tar) | | ResNet50-vd-FPN | Faster | DIOU | 12 | 2 | 1x | 22.94 | 39.2 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_diou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/iou_loss/faster_rcnn_r50_vd_fpn_diou_loss_1x.yml) |
| ResNet50-vd-FPN | Faster | CIOU | 12 | 2 | 1x | 22.95 | 39.6 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_ciou_loss_1x.tar) | | ResNet50-vd-FPN | Faster | CIOU | 12 | 2 | 1x | 22.95 | 39.6 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_ciou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/iou_loss/faster_rcnn_r50_vd_fpn_ciou_loss_1x.yml) |
configs/libra_rcnn/README.md
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...@@ -17,7 +17,7 @@ ...@@ -17,7 +17,7 @@
## Model Zoo ## Model Zoo
| Backbone | Type | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | | Backbone | Type | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | Configs |
| :---------------------- | :-------------: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | | :---------------------- | :-------------: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
| ResNet50-vd-BFP | Faster | 2 | 1x | 18.247 | 40.5 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/libra_rcnn_r50_vd_fpn_1x.tar) | | ResNet50-vd-BFP | Faster | 2 | 1x | 18.247 | 40.5 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/libra_rcnn_r50_vd_fpn_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/libra_rcnn/libra_rcnn_r50_vd_fpn_1x.yml) |
| ResNet101-vd-BFP | Faster | 2 | 1x | 14.865 | 42.5 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/libra_rcnn_r101_vd_fpn_1x.tar) | | ResNet101-vd-BFP | Faster | 2 | 1x | 14.865 | 42.5 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/libra_rcnn_r101_vd_fpn_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/libra_rcnn/libra_rcnn_r101_vd_fpn_1x.yml) |
configs/rcnn_server_side_det/README.md
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...@@ -7,7 +7,7 @@ ...@@ -7,7 +7,7 @@
## Model Zoo ## Model Zoo
| Backbone | Type | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | | Backbone | Type | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | Configs |
| :---------------------- | :-------------: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | | :---------------------- | :-------------: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
| ResNet50-vd-FPN-Dcnv2 | Faster | 2 | 3x | 61.425 | 41.6 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) | | ResNet50-vd-FPN-Dcnv2 | Faster | 2 | 3x | 61.425 | 41.6 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_server_side_det/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml) |
| ResNet50-vd-FPN-Dcnv2 | Cascade Faster | 2 | 3x | 20.001 | 47.8 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) | | ResNet50-vd-FPN-Dcnv2 | Cascade Faster | 2 | 3x | 20.001 | 47.8 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_server_side_det/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml) |
configs/res2net/README.md
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...@@ -28,9 +28,9 @@ ...@@ -28,9 +28,9 @@
## Model Zoo ## Model Zoo
| Backbone | Type | deformable Conv | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | | Backbone | Type | deformable Conv | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | Configs |
| :---------------------- | :------------- | :---: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | | :---------------------- | :------------- | :---: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
| Res2Net50-FPN | Faster | False | 2 | 1x | 20.320 | 39.5 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_res2net50_vb_26w_4s_fpn_1x.tar) | | Res2Net50-FPN | Faster | False | 2 | 1x | 20.320 | 39.5 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_res2net50_vb_26w_4s_fpn_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_1x.yml) |
| Res2Net50-FPN | Mask | False | 2 | 2x | 16.069 | 40.7 | 36.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_res2net50_vb_26w_4s_fpn_2x.tar) | | Res2Net50-FPN | Mask | False | 2 | 2x | 16.069 | 40.7 | 36.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_res2net50_vb_26w_4s_fpn_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_2x.yml) |
| Res2Net50-vd-FPN | Mask | False | 2 | 2x | 15.816 | 40.9 | 36.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_res2net50_vd_26w_4s_fpn_2x.tar) | | Res2Net50-vd-FPN | Mask | False | 2 | 2x | 15.816 | 40.9 | 36.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_res2net50_vd_26w_4s_fpn_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_2x.yml) |
| Res2Net50-vd-FPN | Mask | True | 2 | 2x | 14.478 | 43.5 | 38.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_res2net50_vd_26w_4s_fpn_dcnv2_1x.tar) | | Res2Net50-vd-FPN | Mask | True | 2 | 2x | 14.478 | 43.5 | 38.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_res2net50_vd_26w_4s_fpn_dcnv2_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_dcnv2_1x.yml) |
docs/MODEL_ZOO.md
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docs/MODEL_ZOO_cn.md
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此差异已折叠。
docs/advanced_tutorials/inference/BENCHMARK_INFER_cn.md
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...@@ -10,7 +10,7 @@ ...@@ -10,7 +10,7 @@
- 测试方式: - 测试方式:
- 为了方便比较不同模型的推理速度，输入采用同样大小的图片，为 3x640x640，采用 `demo/000000014439_640x640.jpg` 图片。 - 为了方便比较不同模型的推理速度，输入采用同样大小的图片，为 3x640x640，采用 `demo/000000014439_640x640.jpg` 图片。
- Batch Size=1 - Batch Size=1
- 去掉前10轮warmup时间，测试100轮的平均时间，单位ms/image，包括输入数据拷贝至GPU的时间、计算时间、数据拷贝只CPU的时间。 - 去掉前10轮warmup时间，测试100轮的平均时间，单位ms/image，包括输入数据拷贝至GPU的时间、计算时间、数据拷贝至CPU的时间。
- 采用Fluid C++预测引擎: 包含Fluid C++预测、Fluid-TensorRT预测，下面同时测试了Float32 (FP32) 和Float16 (FP16)的推理速度。 - 采用Fluid C++预测引擎: 包含Fluid C++预测、Fluid-TensorRT预测，下面同时测试了Float32 (FP32) 和Float16 (FP16)的推理速度。
- 测试时开启了 FLAGS_cudnn_exhaustive_search=True，使用exhaustive方式搜索卷积计算算法。 - 测试时开启了 FLAGS_cudnn_exhaustive_search=True，使用exhaustive方式搜索卷积计算算法。
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2.启动训练模型 2.启动训练模型
```bash ```bash
python tools/train.py -c configs/obj365/cascade_rcnn_dcnv2_se154_vd_fpn_gn.yml python tools/train.py -c configs/obj365/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas.yml
``` ```
3.模型预测结果 3.模型预测结果
| 模型 | 验证集 mAP | 下载链接 | | 模型 | 验证集 mAP | 下载链接 | 配置文件 |
| :-----------------: | :--------: | :----------------------------------------------------------: | | :-----------------: | :--------: | :----------------------------------------------------------: | :--------: |
| CACascadeRCNN SE154 | 31.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas_obj365.tar) | | CACascadeRCNN SE154 | 31.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas_obj365.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/obj365/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas.yml) |
## 模型效果 ## 模型效果
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We provide some models implemented by PaddlePaddle to detect objects in specific scenarios, users can download the models and use them in these scenarios. We provide some models implemented by PaddlePaddle to detect objects in specific scenarios, users can download the models and use them in these scenarios.
| Task | Algorithm | Box AP | Download | | Task | Algorithm | Box AP | Download | Configs |
|:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: | |:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: |:------:|
| Vehicle Detection | YOLOv3 | 54.5 | [model](https://paddlemodels.bj.bcebos.com/object_detection/vehicle_yolov3_darknet.tar) | | Vehicle Detection | YOLOv3 | 54.5 | [model](https://paddlemodels.bj.bcebos.com/object_detection/vehicle_yolov3_darknet.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/contrib/VehicleDetection/vehicle_yolov3_darknet.yml) |
| Pedestrian Detection | YOLOv3 | 51.8 | [model](https://paddlemodels.bj.bcebos.com/object_detection/pedestrian_yolov3_darknet.tar) | | Pedestrian Detection | YOLOv3 | 51.8 | [model](https://paddlemodels.bj.bcebos.com/object_detection/pedestrian_yolov3_darknet.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/contrib/PedestrianDetection/pedestrian_yolov3_darknet.yml) |
## Vehicle Detection ## Vehicle Detection
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我们提供了针对不同场景的基于PaddlePaddle的检测模型，用户可以下载模型进行使用。 我们提供了针对不同场景的基于PaddlePaddle的检测模型，用户可以下载模型进行使用。
| 任务 | 算法 | 精度(Box AP) | 下载 | | 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://paddlemodels.bj.bcebos.com/object_detection/vehicle_yolov3_darknet.tar) | | 车辆检测 | YOLOv3 | 54.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/vehicle_yolov3_darknet.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/contrib/VehicleDetection/vehicle_yolov3_darknet.yml) |
| 行人检测 | YOLOv3 | 51.8 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/pedestrian_yolov3_darknet.tar) | | 行人检测 | YOLOv3 | 51.8 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/pedestrian_yolov3_darknet.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/contrib/PedestrianDetection/pedestrian_yolov3_darknet.yml) |
## 车辆检测（Vehicle Detection） ## 车辆检测（Vehicle Detection）
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#### WIDER-FACE数据集上的mAP #### WIDER-FACE数据集上的mAP
| 网络结构 | 类型 | 输入尺寸 | 图片个数/GPU | 学习率策略 | Easy Set | Medium Set | Hard Set | 下载 | | 网络结构 | 类型 | 输入尺寸 | 图片个数/GPU | 学习率策略 | Easy Set | Medium Set | Hard Set | 下载 | 配置文件 |
|:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:| |:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:|:--------:|
| BlazeFace | 原始版本 | 640 | 8 | 32w | **0.915** | **0.892** | **0.797** | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) | | BlazeFace | 原始版本 | 640 | 8 | 32w | **0.915** | **0.892** | **0.797** | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface.yml) |
| BlazeFace | Lite版本 | 640 | 8 | 32w | 0.909 | 0.885 | 0.781 | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar) | | BlazeFace | Lite版本 | 640 | 8 | 32w | 0.909 | 0.885 | 0.781 | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface.yml) |
| BlazeFace | NAS版本 | 640 | 8 | 32w | 0.837 | 0.807 | 0.658 | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) | | BlazeFace | NAS版本 | 640 | 8 | 32w | 0.837 | 0.807 | 0.658 | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface_nas.yml) |
| BlazeFace | NAS_V2版本 | 640 | 8 | 32W | 0.870 | 0.837 | 0.685 | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) | BlazeFace | NAS_V2版本 | 640 | 8 | 32W | 0.870 | 0.837 | 0.685 | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface_nas_v2.yml) |
| FaceBoxes | 原始版本 | 640 | 8 | 32w | 0.878 | 0.851 | 0.576 | [模型](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_original.tar) | | FaceBoxes | 原始版本 | 640 | 8 | 32w | 0.878 | 0.851 | 0.576 | [模型](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_original.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/faceboxes.yml) |
| FaceBoxes | Lite版本 | 640 | 8 | 32w | 0.901 | 0.875 | 0.760 | [模型](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_lite.tar) | | FaceBoxes | Lite版本 | 640 | 8 | 32w | 0.901 | 0.875 | 0.760 | [模型](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_lite.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/faceboxes_lite.yml) |
**注意:** **注意:**
- 我们使用`tools/face_eval.py`中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)。 - 我们使用`tools/face_eval.py`中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)。
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#### mAP in WIDER FACE #### mAP in WIDER FACE
| Architecture | Type | Size | Img/gpu | Lr schd | Easy Set | Medium Set | Hard Set | Download | | Architecture | Type | Size | Img/gpu | Lr schd | Easy Set | Medium Set | Hard Set | Download | Configs |
|:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:| |:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:|:--------:|
| BlazeFace | Original | 640 | 8 | 32w | **0.915** | **0.892** | **0.797** | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) | | BlazeFace | Original | 640 | 8 | 32w | **0.915** | **0.892** | **0.797** | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface.yml) |
| BlazeFace | Lite | 640 | 8 | 32w | 0.909 | 0.885 | 0.781 | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar) | | BlazeFace | Lite | 640 | 8 | 32w | 0.909 | 0.885 | 0.781 | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface.yml) |
| BlazeFace | NAS | 640 | 8 | 32w | 0.837 | 0.807 | 0.658 | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) | | BlazeFace | NAS | 640 | 8 | 32w | 0.837 | 0.807 | 0.658 | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface_nas.yml) |
| BlazeFace | NAS_V2 | 640 | 8 | 32W | 0.870 | 0.837 | 0.685 | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) | BlazeFace | NAS_V2 | 640 | 8 | 32W | 0.870 | 0.837 | 0.685 | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface_nas_v2.yml) |
| FaceBoxes | Original | 640 | 8 | 32w | 0.878 | 0.851 | 0.576 | [model](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_original.tar) | | FaceBoxes | Original | 640 | 8 | 32w | 0.878 | 0.851 | 0.576 | [model](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_original.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/faceboxes.yml) |
| FaceBoxes | Lite | 640 | 8 | 32w | 0.901 | 0.875 | 0.760 | [model](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_lite.tar) | | FaceBoxes | Lite | 640 | 8 | 32w | 0.901 | 0.875 | 0.760 | [model](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_lite.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/faceboxes_lite.yml) |
**NOTES:** **NOTES:**
- Get mAP in `Easy/Medium/Hard Set` by multi-scale evaluation in `tools/face_eval.py`. - Get mAP in `Easy/Medium/Hard Set` by multi-scale evaluation in `tools/face_eval.py`.
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OIDV5模型训练结果如下。 OIDV5模型训练结果如下。
| 模型结构 | Public/Private Score | 下载链接 | | 模型结构 | Public/Private Score | 下载链接 | 配置文件 |
| :-----------------: | :--------: | :----------------------------------------------------------: | | :-----------------: | :--------: | :----------------------------------------------------------: | :--------: |
| CascadeCARCNN-FPN-Dcnv2-Nonlocal ResNet200-vd | 0.62690/0.59459 | [模型](https://paddlemodels.bj.bcebos.com/object_detection/oidv5_cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) | | CascadeCARCNN-FPN-Dcnv2-Nonlocal ResNet200-vd | 0.62690/0.59459 | [模型](https://paddlemodels.bj.bcebos.com/object_detection/oidv5_cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/oidv5/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.yml) |
此外，为验证模型的性能，团队基于该模型结构，也训练了针对COCO2017和Objects365 Dataset的模型，模型和验证集指标如下表。 此外，为验证模型的性能，PaddleDetection基于该模型结构，也训练了针对COCO2017和Objects365 Dataset的模型，模型和验证集指标如下表。
| 模型结构 | 数据集 | 验证集mAP | 下载链接 | | 模型结构 | 数据集 | 验证集mAP | 下载链接 | 配置文件 |
| :-----------------: | :--------: | :--------: | :----------------------------------------------------------: | | :-----------------: | :--------: | :--------: | :----------------------------------------------------------: | :--------: |
| CascadeCARCNN-FPN-Dcnv2-Nonlocal ResNet200-vd | COCO2017 | 51.7% | [模型](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) | | CascadeCARCNN-FPN-Dcnv2-Nonlocal ResNet200-vd | COCO2017 | 51.7% | [模型](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/obj365/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.yml) |
| CascadeCARCNN-FPN-Dcnv2-Nonlocal ResNet200-vd | Objects365 | 34.5% | [模型](https://paddlemodels.bj.bcebos.com/object_detection/obj365_cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) | | CascadeCARCNN-FPN-Dcnv2-Nonlocal ResNet200-vd | Objects365 | 34.5% | [模型](https://paddlemodels.bj.bcebos.com/object_detection/obj365_cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/obj365/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.yml) |
COCO和Objects365 Dataset数据格式相同，目前只支持预测和评估。 COCO和Objects365 Dataset数据格式相同，目前只支持预测和评估。
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```bash ```bash
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python tools/train.py -c configs/dcn/yolov3_r50vd_dcn_iouloss_obj365_pretrained_coco.yml python tools/train.py -c configs/dcn/yolov3_r50vd_dcn_db_iouloss_obj365_pretrained_coco.yml
``` ```
更多模型参数请使用``python tools/train.py --help``查看，或参考[训练、评估及参数说明](../tutorials/GETTING_STARTED_cn.md)文档 更多模型参数请使用``python tools/train.py --help``查看，或参考[训练、评估及参数说明](../tutorials/GETTING_STARTED_cn.md)文档
### 模型效果 ### 模型效果
| 模型 | 预训练模型 | 验证集 mAP | P4预测速度 | 下载 | | 模型 | 预训练模型 | 验证集 mAP | P4预测速度 | 下载 | 配置文件 |
| :--------------------------------------: | :----------------------------------------------------------: | :--------: | :------------------------------------: | :----------------------------------------------------------: | | :--------------------------------------: | :----------------------------------------------------------: | :--------: | :------------------------------------: | :----------------------------------------------------------: | :--------: |
| YOLOv3 DarkNet | [DarkNet pretrain](https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar) | 38.9 | 原生：88.3ms<br>tensorRT-FP32: 42.5ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | | YOLOv3 DarkNet | [DarkNet pretrain](https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar) | 38.9 | 原生：88.3ms<br>tensorRT-FP32: 42.5ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov3_darknet.yml) |
| YOLOv3 ResNet50_vd DCN | [ImageNet pretrain](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar) | 39.1 | 原生：74.4ms<br>tensorRT-FP32: 35.2ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_imagenet.tar) | | YOLOv3 ResNet50_vd DCN | [ImageNet pretrain](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar) | 39.1 | 原生：74.4ms<br>tensorRT-FP32: 35.2ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_imagenet.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn/yolov3_r50vd_dcn.yml) |
| YOLOv3 ResNet50_vd DCN | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) | 42.5 | 原生：74.4ms<br>tensorRT-FP32: 35.2ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_v2.tar) | | YOLOv3 ResNet50_vd DCN | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) | 42.5 | 原生：74.4ms<br>tensorRT-FP32: 35.2ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_v2.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn/yolov3_r50vd_dcn_obj365_pretrained_coco.yml) |
| YOLOv3 ResNet50_vd DCN DropBlock | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) | 42.8 | 原生：74.4ms<br/>tensorRT-FP32: 35.2ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_dropblock.tar) | | YOLOv3 ResNet50_vd DCN DropBlock | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) | 42.8 | 原生：74.4ms<br/>tensorRT-FP32: 35.2ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_dropblock.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn/yolov3_r50vd_dcn_db_obj365_pretrained_coco.yml) |
| YOLOv3 ResNet50_vd DCN DropBlock IoULoss | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) | 43.2 | 原生：74.4ms<br/>tensorRT-FP32: 35.2ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_dropblock_iouloss.tar) | | YOLOv3 ResNet50_vd DCN DropBlock IoULoss | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) | 43.2 | 原生：74.4ms<br/>tensorRT-FP32: 35.2ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_dropblock_iouloss.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn/yolov3_r50vd_dcn_db_iouloss_obj365_pretrained_coco.yml) |
| YOLOv3 ResNet50_vd DCN DropBlock IoU-Aware | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) | 43.6 | 原生：74.4ms<br/>tensorRT-FP32: 35.2ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_db_iouaware_obj365_pretrained_coco.pdparams) | | YOLOv3 ResNet50_vd DCN DropBlock IoU-Aware | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) | 43.6 | 原生：74.4ms<br/>tensorRT-FP32: 35.2ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_db_iouaware_obj365_pretrained_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn/yolov3_r50vd_dcn_db_iouaware_obj365_pretrained_coco.yml) |
docs/tutorials/QUICK_STARTED_cn.md
浏览文件 @ 521a4a6a
...@@ -13,7 +13,7 @@ export CUDA_VISIBLE_DEVICES=0 ...@@ -13,7 +13,7 @@ export CUDA_VISIBLE_DEVICES=0
## 数据准备 ## 数据准备
数据集参考[Kaggle数据集](https://www.kaggle.com/mbkinaci/fruit-images-for-object-detection)，其中训练数据集240张图片，测试数据集60张图片，数据类别为3类：苹果，橘子，香蕉。[下载链接](https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar)。数据下载后分别解压即可, 数据准备脚本位于[download_fruit.py](../../dataset/fruit/download_fruit.py)。下载数据方式如下： 数据集参考[Kaggle数据集](https://www.kaggle.com/mbkinaci/fruit-images-for-object-detection)，其中训练数据集240张图片，测试数据集60张图片，数据类别为3类：苹果，橘子，香蕉。[下载链接](https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar)。数据下载后分别解压即可, 数据准备脚本位于[download_fruit.py](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dataset/fruit/download_fruit.py)。下载数据方式如下：
```bash ```bash
python dataset/fruit/download_fruit.py python dataset/fruit/download_fruit.py
......
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