diff --git a/configs/keypoint/README.md b/configs/keypoint/README.md index a71d1fceec0cc84f95fd26e5d57da7b860e767d4..1dc5e9d028d5165126fe891841382be8983cd5bf 100644 --- a/configs/keypoint/README.md +++ b/configs/keypoint/README.md @@ -24,6 +24,7 @@ - [Bottom-Up模型独立部署](#bottom-up模型独立部署) - [与多目标跟踪联合部署](#与多目标跟踪模型fairmot联合部署) - [完整部署教程及Demo](#4完整部署教程及Demo) + - [自定义数据训练](#自定义数据训练) - [BenchMark](#benchmark) @@ -52,6 +53,33 @@ PaddleDetection 关键点检测能力紧跟业内最新最优算法方案，包 | [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) | +*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) | + +## 模型库 + +COCO数据集 +| 模型 | 方案 |输入尺寸 | AP(coco val) | 模型下载 | 配置文件 | +| :---------------- | -------- | :----------: | :----------------------------------------------------------: | ----------------------------------------------------| ------- | +| HigherHRNet-w32 |Bottom-Up| 512 | 67.1 | [higherhrnet_hrnet_w32_512.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_512.yml) | +| HigherHRNet-w32 | Bottom-Up| 640 | 68.3 | [higherhrnet_hrnet_w32_640.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_640.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_640.yml) | +| HigherHRNet-w32+SWAHR |Bottom-Up| 512 | 68.9 | [higherhrnet_hrnet_w32_512_swahr.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512_swahr.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_512_swahr.yml) | +| HRNet-w32 | Top-Down| 256x192 | 76.9 | [hrnet_w32_256x192.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) | [config](./hrnet/hrnet_w32_256x192.yml) | +| HRNet-w32 |Top-Down| 384x288 | 77.8 | [hrnet_w32_384x288.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) | [config](./hrnet/hrnet_w32_384x288.yml) | +| HRNet-w32+DarkPose |Top-Down| 256x192 | 78.0 | [dark_hrnet_w32_256x192.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/dark_hrnet_w32_256x192.pdparams) | [config](./hrnet/dark_hrnet_w32_256x192.yml) | +| HRNet-w32+DarkPose |Top-Down| 384x288 | 78.3 | [dark_hrnet_w32_384x288.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/dark_hrnet_w32_384x288.pdparams) | [config](./hrnet/dark_hrnet_w32_384x288.yml) | +| WiderNaiveHRNet-18 | Top-Down|256x192 | 67.6(+DARK 68.4) | [wider_naive_hrnet_18_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/wider_naive_hrnet_18_256x192_coco.pdparams) | [config](./lite_hrnet/wider_naive_hrnet_18_256x192_coco.yml) | +| LiteHRNet-18 |Top-Down| 256x192 | 66.5 | [lite_hrnet_18_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_18_256x192_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_18_256x192_coco.yml) | +| LiteHRNet-18 |Top-Down| 384x288 | 69.7 | [lite_hrnet_18_384x288_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_18_384x288_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_18_384x288_coco.yml) | +| LiteHRNet-30 | Top-Down|256x192 | 69.4 | [lite_hrnet_30_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_30_256x192_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_30_256x192_coco.yml) | +| LiteHRNet-30 |Top-Down| 384x288 | 72.5 | [lite_hrnet_30_384x288_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_30_384x288_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_30_384x288_coco.yml) | ## 模型库 @@ -143,7 +171,8 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/infer.py -c configs/keypoint/higherhrnet/hi ```shell #导出检测模型 -python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams + +python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams #导出关键点模型 python tools/export_model.py -c configs/keypoint/hrnet/hrnet_w32_256x192.yml -o weights=https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams @@ -177,6 +206,7 @@ python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inferenc ### 4、完整部署教程及Demo + ​ 我们提供了PaddleInference(服务器端)、PaddleLite(移动端)、第三方部署(MNN、OpenVino)支持。无需依赖训练代码，deploy文件夹下相应文件夹提供独立完整部署代码。 详见 [部署文档](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/deploy/README.md)介绍。 ## 自定义数据训练 diff --git a/configs/keypoint/README_en.md b/configs/keypoint/README_en.md index 1bc4cc0ac41955ee4bf93a89f1ad3898f8ed43ab..2c1ba042d385cbd120cca3d01650b4ecc08be748 100644 --- a/configs/keypoint/README_en.md +++ b/configs/keypoint/README_en.md @@ -20,14 +20,17 @@ - [Deployment for Bottom-Up models](#deployment-for-bottom-up-models) - [Joint Inference with Multi-Object Tracking Model FairMOT](#joint-inference-with-multi-object-tracking-model-fairmot) - [Complete Deploy Instruction and Demo](#4Complete-Deploy-Instruction-and-Demo) + - [Train with custom data](#Train-with-custom-data) + - [BenchMark](#benchmark) ## Introduction -The keypoint detection part in PaddleDetection follows the state-of-the-art algorithm closely, including Top-Down and Bottom-Up methods, which can satisfy the different needs of users. +The keypoint detection part in PaddleDetection follows the state-of-the-art algorithm closely, including Top-Down and Bottom-Up methods, which can satisfy the different needs of users. + +Top-Down detects the object first and then detect the specific keypoint. The accuracy of Top-Down models will be higher, but the time required will increase by the number of objects. -Top-Down detects the object first and then detect the specific keypoint. The accuracy of Top-Down models will be higher, but the time required will increase by the number of objects. Differently, Bottom-Up detects the point first and then group or connect those points to form several instances of human pose. The speed of Bottom-Up is fixed and will not increase by the number of objects, but the accuracy will be lower. @@ -43,6 +46,7 @@ At the same time, PaddleDetection provides [PP-TinyPose](./tiny_pose/README_en.m + | 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) | @@ -53,6 +57,7 @@ At the same time, PaddleDetection provides [PP-TinyPose](./tiny_pose/README_en.m ### Terminal Server + | 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) | @@ -149,7 +154,9 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/infer.py -c configs/keypoint/higherhrnet/hi ```shell #Export Detection Model -python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams + +python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams + #Export Keypoint Model python tools/export_model.py -c configs/keypoint/hrnet/hrnet_w32_256x192.yml -o weights=https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams