- Release GPU SOTA object detection series models (s/m/l/x) [PP-YOLOE](configs/ppyoloe), supporting s/m/l/x version, achieving mAP as 51.4% on COCO test dataset and 78.1 FPS on Nvidia V100 by PP-YOLOE-l, supporting AMP training and its training speed is 33% faster than PP-YOLOv2.
- Release GPU SOTA object detection series models (s/m/l/x) [PP-YOLOE](configs/ppyoloe), supporting s/m/l/x version, achieving mAP as 51.6% on COCO test dataset and 78.1 FPS on Nvidia V100 by PP-YOLOE-l, supporting AMP training and its training speed is 33% faster than PP-YOLOv2.
- Release enhanced models of [PP-PicoDet](configs/picodet), including PP-PicoDet-XS model with 0.7M parameters, its mAP promoted ~2% on COCO, inference speed accelerated 63% on CPU, and post-processing integrated into the network to optimize deployment pipeline.
- Release real-time human analysis tool [PP-Human](deploy/pphuman), which is based on data from real-life situations, supporting pedestrian detection, attribute recognition, human tracking, multi-camera tracking, human statistics and action recognition.
- Release [YOLOX](configs/yolox), supporting nano/tiny/s/m/l/x version, achieving mAP as 51.8% on COCO val dataset by YOLOX-x.
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@@ -250,7 +250,7 @@ The relationship between COCO mAP and FPS on Tesla V100 of representative models
-`PP-YOLO v2` is optimized version of `PP-YOLO` which has mAP of 49.5% and 68.9FPS on Tesla V100
-`PP-YOLOE` is optimized version of `PP-YOLO v2` which has mAP of 51.4% and 78.1FPS on Tesla V100
-`PP-YOLOE` is optimized version of `PP-YOLO v2` which has mAP of 51.6% and 78.1FPS on Tesla V100
- All these models can be get in [Model Zoo](#ModelZoo)
PP-YOLOE-l achieves 51.4 mAP on COCO test-dev2017 dataset with 78.1 FPS on Tesla V100. While using TensorRT FP16, PP-YOLOE-l can be further accelerated to 149.2 FPS. PP-YOLOE-s/m/x also have excellent accuracy and speed performance, which can be found in [Model Zoo](#Model-Zoo)
PP-YOLOE-l achieves 51.6 mAP on COCO test-dev2017 dataset with 78.1 FPS on Tesla V100. While using TensorRT FP16, PP-YOLOE-l can be further accelerated to 149.2 FPS. PP-YOLOE-s/m/x also have excellent accuracy and speed performance, which can be found in [Model Zoo](#Model-Zoo)
PP-YOLOE is composed of following methods:
- Scalable backbone and neck
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@@ -26,10 +26,10 @@ PP-YOLOE is composed of following methods:
@@ -43,15 +43,7 @@ PP-YOLOE is composed of following methods:
**Notes:**
- PP-YOLOE is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset，all the model weights are trained for **300 epoches**.
- The model weights in the table of Comprehensive Metrics are **the same as** that in the original Model Zoo, and evaluated on **val2017**. To reproduce the metrics, just modify the setting of `nms` in [ppyoloe_crn.yml](_base_/ppyoloe_crn.yml):
```
nms:
name: MultiClassNMS
nms_top_k: 10000
keep_top_k: 300
score_threshold: 0.01
nms_threshold: 0.7
```
- The model weights in the table of Comprehensive Metrics are **the same as** that in the original Model Zoo, and evaluated on **val2017**.
- PP-YOLOE used 8 GPUs for mixed precision training, if **GPU number** or **mini-batch size** is changed, **learning rate** should be adjusted according to the formula **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)**.
- 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.
- Refer to [Speed testing](#Speed-testing) to reproduce the speed testing results of PP-YOLOE.
- Release PP-YOLOE object detection models, achieve mAP as 51.4% on COCO test dataset and 78.1 FPS on Nvidia V100 by PP-YOLOE-l, reach SOTA performance for object detection on GPU``
- Release PP-YOLOE object detection models, achieve mAP as 51.6% on COCO test dataset and 78.1 FPS on Nvidia V100 by PP-YOLOE-l, reach SOTA performance for object detection on GPU``
- Release series models: s/m/l/x, and support deployment base on TensorRT & ONNX
- Spport AMP training and training speed is 33% faster than PP-YOLOv2
