diff --git a/configs/keypoint/README.md b/configs/keypoint/README.md index 0789a6488df4991869dbe3d909966ead5807ff8d..a71d1fceec0cc84f95fd26e5d57da7b860e767d4 100644 --- a/configs/keypoint/README.md +++ b/configs/keypoint/README.md @@ -37,19 +37,21 @@ PaddleDetection 关键点检测能力紧跟业内最新最优算法方案，包 ### 移动端模型推荐 -| 检测模型 | 关键点模型 | 输入尺寸 | COCO数据集精度 | 平均推理耗时 (FP16) | 模型权重 | Paddle-Lite部署模型（FP16) | -|:--------------------------------------------------------------------------------------------------- |:------------------------------------- |:-------------------------:|:------------------------:|:---------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| -| [PicoDet-S-Pedestrian](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [PP-TinyPose](./tinypose_128x96.yml) | 检测：192x192
关键点：128x96 | 检测mAP：29.0
关键点AP：58.1 | 检测耗时：2.37ms
关键点耗时：3.27ms | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)
[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb)
[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) | -| [PicoDet-S-Pedestrian](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [PP-TinyPose](./tinypose_256x192.yml) | 检测：320x320
关键点：256x192 | 检测mAP：38.5
关键点AP：68.8 | 检测耗时：6.30ms
关键点耗时：8.33ms | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)
[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb)
[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) | +| 检测模型 | 关键点模型 | 输入尺寸 | COCO数据集精度 | 平均推理耗时 (FP16) | 参数量 （M） | Flops (G) | 模型权重 | Paddle-Lite部署模型（FP16) | +| :----------------------------------------------------------- | :------------------------------------ | :------------------------------: | :-----------------------------: | :------------------------------------: | --------------------------- | :-------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | +| [PicoDet-S-Pedestrian](../picodet/legacy_model/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [PP-TinyPose](./tinypose_128x96.yml) | 检测：192x192
关键点：128x96 | 检测mAP：29.0
关键点AP：58.1 | 检测耗时：2.37ms
关键点耗时：3.27ms | 检测：1.18
关键点：1.36 | 检测：0.35
关键点：0.08 | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)
[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb)
[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) | +| [PicoDet-S-Pedestrian](../picodet/legacy_model/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [PP-TinyPose](./tinypose_256x192.yml) | 检测：320x320
关键点：256x192 | 检测mAP：38.5
关键点AP：68.8 | 检测耗时：6.30ms
关键点耗时：8.33ms | 检测：1.18
关键点：1.36 | 检测：0.97
关键点：0.32 | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)
[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb)
[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) | + *详细关于PP-TinyPose的使用请参考[文档]((./tiny_pose/README.md))。 ### 服务端模型推荐 -| 检测模型 | 关键点模型 | 输入尺寸 | COCO数据集精度 | 模型权重 | -|:----------------------------------------------------------------------------------------------------------------------------- |:------------------------------------------ |:-------------------------:|:------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------:| -| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_384x288.yml) | 检测：640x640
关键点：384x288 | 检测mAP：49.5
关键点AP：77.8 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[关键点](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) | -| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_256x192.yml) | 检测：640x640
关键点：256x192 | 检测mAP：49.5
关键点AP：76.9 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[关键点](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) | +| 检测模型 | 关键点模型 | 输入尺寸 | COCO数据集精度 | 参数量 （M） | Flops (G) | 模型权重 | +| :----------------------------------------------------------- | :----------------------------------------- | :------------------------------: | :-----------------------------: | :----------------------: | :----------------------: | :----------------------------------------------------------: | +| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_384x288.yml) | 检测：640x640
关键点：384x288 | 检测mAP：49.5
关键点AP：77.8 | 检测：54.6
关键点：28.6 | 检测：115.8
关键点：17.3 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[关键点](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) | +| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_256x192.yml) | 检测：640x640
关键点：256x192 | 检测mAP：49.5
关键点AP：76.9 | 检测：54.6
关键点：28.6 | 检测：115.8
关键点：7.68 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[关键点](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) | + ## 模型库 diff --git a/configs/keypoint/README_en.md b/configs/keypoint/README_en.md index 7c1979ce7efa93d6755dea70c64a3c371c9aa87e..1bc4cc0ac41955ee4bf93a89f1ad3898f8ed43ab 100644 --- a/configs/keypoint/README_en.md +++ b/configs/keypoint/README_en.md @@ -41,19 +41,23 @@ At the same time, PaddleDetection provides [PP-TinyPose](./tiny_pose/README_en.m ### Mobile Terminal -| Detection Model | Keypoint Model | Input Size | Accuracy of COCO | Average Inference Time (FP16) | Model Weight | Paddle-Lite Inference Model（FP16) | -|:--------------------------------------------------------------------------------------------------- |:------------------------------------- |:-------------------------------------:|:--------------------------------------:|:-----------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| -| [PicoDet-S-Pedestrian](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [PP-TinyPose](./tinypose_128x96.yml) | Detection：192x192
Keypoint：128x96 | Detection mAP：29.0
Keypoint AP：58.1 | Detection：2.37ms
Keypoint：3.27ms | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)
[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb)
[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) | -| [PicoDet-S-Pedestrian](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [PP-TinyPose](./tinypose_256x192.yml) | Detection：320x320
Keypoint：256x192 | Detection mAP：38.5
Keypoint AP：68.8 | Detection：6.30ms
Keypoint：8.33ms | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)
[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb)
[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) | + + +| Detection Model | Keypoint Model | Input Size | Accuracy of COCO | Average Inference Time (FP16) | Params (M) | Flops (G) | Model Weight | Paddle-Lite Inference Model（FP16) | +| :----------------------------------------------------------- | :------------------------------------ | :-------------------------------------: | :--------------------------------------: | :-----------------------------------: | :--------------------------------: | :--------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | +| [PicoDet-S-Pedestrian](../picodet/legacy_model/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [PP-TinyPose](./tinypose_128x96.yml) | Detection：192x192
Keypoint：128x96 | Detection mAP：29.0
Keypoint AP：58.1 | Detection：2.37ms
Keypoint：3.27ms | Detection：1.18
Keypoint：1.36 | Detection：0.35
Keypoint：0.08 | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)
[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb)
[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) | +| [PicoDet-S-Pedestrian](../picodet/legacy_model/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [PP-TinyPose](./tinypose_256x192.yml) | Detection：320x320
Keypoint：256x192 | Detection mAP：38.5
Keypoint AP：68.8 | Detection：6.30ms
Keypoint：8.33ms | Detection：1.18
Keypoint：1.36 | Detection：0.97
Keypoint：0.32 | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)
[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb)
[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) | + *Specific documents of PP-TinyPose, please refer to [Document]((./tiny_pose/README.md))。 ### Terminal Server -| Detection Model | Keypoint Model | Input Size | Accuracy of COCO | Model Weight | -|:----------------------------------------------------------------------------------------------------------------------------- |:------------------------------------------ |:-------------------------------------:|:--------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| -| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_384x288.yml) | Detection：640x640
Keypoint：384x288 | Detection mAP：49.5
Keypoint AP：77.8 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[Keypoint](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) | -| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_256x192.yml) | Detection：640x640
Keypoint：256x192 | Detection mAP：49.5
Keypoint AP：76.9 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[Keypoint](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) | +| Detection Model | Keypoint Model | Input Size | Accuracy of COCO | Params (M) | Flops (G) | Model Weight | +| :----------------------------------------------------------- | :----------------------------------------- | :-------------------------------------: | :--------------------------------------: | :-----------------------------: | :-----------------------------: | :----------------------------------------------------------: | +| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_384x288.yml) | Detection：640x640
Keypoint：384x288 | Detection mAP：49.5
Keypoint AP：77.8 | Detection：54.6
Keypoint：28.6 | Detection：115.8
Keypoint：17.3 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[Keypoint](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) | +| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_256x192.yml) | Detection：640x640
Keypoint：256x192 | Detection mAP：49.5
Keypoint AP：76.9 | Detection：54.6
Keypoint：28.6 | Detection：115.8
Keypoint：7.68 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[Keypoint](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) | + ## Model Zoo