diff --git a/configs/keypoint/tiny_pose/README.md b/configs/keypoint/tiny_pose/README.md index 6d9c5be02d4eccfff00abc611da96296fab2223c..28177f907477b18060a29ab892918cd498da8e29 100644 --- a/configs/keypoint/tiny_pose/README.md +++ b/configs/keypoint/tiny_pose/README.md @@ -37,16 +37,16 @@ PP-TinyPose是PaddleDetecion针对移动端设备优化的实时关键点检测 ### 关键点检测模型 | 模型 | 输入尺寸 | AP (COCO Val) | 单人推理耗时 (FP32) | 单人推理耗时（FP16) | 配置文件 | 模型权重 | 预测部署模型 | Paddle-Lite部署模型（FP32) | Paddle-Lite部署模型（FP16) | | :---------- | :------: | :-----------: | :-----------------: | :-----------------: | :------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | -| PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./tinypose_128x96.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.tar) | -| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.tar) | +| PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./tinypose_128x96.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_lite.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16_lite.tar) | +| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_lite.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16_lite.tar) | ### 行人检测模型 | 模型 | 输入尺寸 | mAP (COCO Val) | 平均推理耗时 (FP32) | 平均推理耗时 (FP16) | 配置文件 | 模型权重 | 预测部署模型 | Paddle-Lite部署模型（FP32) | Paddle-Lite部署模型（FP16) | | :------------------- | :------: | :------------: | :-----------------: | :-----------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | -| PicoDet-S-Pedestrian | 192*192 | 29.0 | 4.30ms | 2.37ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.tar) | -| PicoDet-S-Pedestrian | 320*320 | 38.5 | 10.26ms | 6.30ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.tar) | +| PicoDet-S-Pedestrian | 192*192 | 29.0 | 4.30ms | 2.37ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_lite.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16_lite.tar) | +| PicoDet-S-Pedestrian | 320*320 | 38.5 | 10.26ms | 6.30ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_lite.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16_lite.tar) | diff --git a/configs/keypoint/tiny_pose/README_en.md b/configs/keypoint/tiny_pose/README_en.md index e632c5b1996b71e5414fb8f596a4a497c9160015..c7147c632e1e915f0f840018cddcfd937f638a27 100644 --- a/configs/keypoint/tiny_pose/README_en.md +++ b/configs/keypoint/tiny_pose/README_en.md @@ -37,14 +37,14 @@ If you want to deploy it on the mobile devives, you also need: ### Keypoint Detection Model | Model | Input Size | AP (COCO Val) | Inference Time for Single Person (FP32)| Inference Time for Single Person（FP16) | Config | Model Weights | Deployment Model | Paddle-Lite Model（FP32) | Paddle-Lite Model（FP16)| | :------------------------ | :-------: | :------: | :------: |:---: | :---: | :---: | :---: | :---: | :---: | -| PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./tinypose_128x96.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.tar) | -| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.tar) | +| PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./tinypose_128x96.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_lite.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16_lite.tar) | +| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_lite.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16_lite.tar) | ### Pedestrian Detection Model | Model | Input Size | mAP (COCO Val) | Average Inference Time (FP32)| Average Inference Time (FP16) | Config | Model Weights | Deployment Model | Paddle-Lite Model（FP32) | Paddle-Lite Model（FP16)| | :------------------------ | :-------: | :------: | :------: | :---: | :---: | :---: | :---: | :---: | :---: | -| PicoDet-S-Pedestrian | 192*192 | 29.0 | 4.30ms | 2.37ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.tar) | -| PicoDet-S-Pedestrian | 320*320 | 38.5 | 10.26ms | 6.30ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.tar) | +| PicoDet-S-Pedestrian | 192*192 | 29.0 | 4.30ms | 2.37ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_lite.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16_lite.tar) | +| PicoDet-S-Pedestrian | 320*320 | 38.5 | 10.26ms | 6.30ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_lite.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16_lite.tar) | **Tips** diff --git a/configs/ppyoloe/README.md b/configs/ppyoloe/README.md index c7d4df8131d905779cc1b85c4e759a0321f9c61a..365c18e765ea0e2e31045d44adca0db9a4f914c8 100644 --- a/configs/ppyoloe/README.md +++ b/configs/ppyoloe/README.md @@ -75,7 +75,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_l_30 ### 4. Deployment -- PaddleInference [Python](../../deploy/python) & [C++](../../deploy/cpp) +- Paddle Inference [Python](../../deploy/python) & [C++](../../deploy/cpp) - [Paddle-TensorRT](../../deploy/TENSOR_RT.md) - [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX) - [PaddleServing](https://github.com/PaddlePaddle/Serving) @@ -86,16 +86,16 @@ For deployment on GPU or benchmarked, model should be first exported to inferenc Exporting PP-YOLOE for Paddle Inference **without TensorRT**, use following command. ```bash -python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams +python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams ``` Exporting PP-YOLOE for Paddle Inference **with TensorRT** for better performance, use following command with extra `-o trt=True` setting. ```bash -python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams trt=True +python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams trt=True ``` -`deploy/python/infer.py` is used to load exported paddle inference model above for inference and benchmark through PaddleInference. +`deploy/python/infer.py` is used to load exported paddle inference model above for inference and benchmark through Paddle Inference. ```bash # inference single image diff --git a/configs/ppyoloe/README_cn.md b/configs/ppyoloe/README_cn.md index 32c259667ada4ffb9e3b46e94a6a620012d1cada..51159b4832ac6ad1bfa3e24e2e3a91b6977fc470 100644 --- a/configs/ppyoloe/README_cn.md +++ b/configs/ppyoloe/README_cn.md @@ -76,7 +76,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_l_30 ### 4. 部署 -- PaddleInference [Python](../../deploy/python) & [C++](../../deploy/cpp) +- Paddle Inference [Python](../../deploy/python) & [C++](../../deploy/cpp) - [Paddle-TensorRT](../../deploy/TENSOR_RT.md) - [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX) - [PaddleServing](https://github.com/PaddlePaddle/Serving) @@ -84,19 +84,19 @@ CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_l_30 PP-YOLOE在GPU上部署或者推理benchmark需要通过`tools/export_model.py`导出模型。 -当你使用PaddleInferenced但不使用TensorRT时，运行以下的命令进行导出 +当你使用Paddle Inference但不使用TensorRT时，运行以下的命令进行导出 ```bash -python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams +python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams ``` -当你使用PaddleInference的TensorRT时，需要指定`-o trt=True`进行导出 +当你使用Paddle Inference的TensorRT时，需要指定`-o trt=True`进行导出 ```bash -python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams trt=True +python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams trt=True ``` -`deploy/python/infer.py`使用上述导出后的PaddleInference模型用于推理和benchnark. +`deploy/python/infer.py`使用上述导出后的Paddle Inference模型用于推理和benchnark. ```bash # 推理单张图片