PP-YOLOE Legacy Model Zoo (2022.03)
August 22, 2022 ยท View on GitHub
Legacy Model Zoo
| Model | Epoch | GPU number | images/GPU | backbone | input shape | Box APval 0.5:0.95 | Box APtest 0.5:0.95 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PP-YOLOE-s | 400 | 8 | 32 | cspresnet-s | 640 | 43.4 | 43.6 | 7.93 | 17.36 | 208.3 | 333.3 | model | config |
| PP-YOLOE-s | 300 | 8 | 32 | cspresnet-s | 640 | 43.0 | 43.2 | 7.93 | 17.36 | 208.3 | 333.3 | model | config |
| PP-YOLOE-m | 300 | 8 | 28 | cspresnet-m | 640 | 49.0 | 49.1 | 23.43 | 49.91 | 123.4 | 208.3 | model | config |
| PP-YOLOE-l | 300 | 8 | 20 | cspresnet-l | 640 | 51.4 | 51.6 | 52.20 | 110.07 | 78.1 | 149.2 | model | config |
| PP-YOLOE-x | 300 | 8 | 16 | cspresnet-x | 640 | 52.3 | 52.4 | 98.42 | 206.59 | 45.0 | 95.2 | model | config |
Comprehensive Metrics
| Model | Epoch | AP0.5:0.95 | AP0.5 | AP0.75 | APsmall | APmedium | APlarge | ARsmall | ARmedium | ARlarge | download | config |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PP-YOLOE-s | 400 | 43.4 | 60.0 | 47.5 | 25.7 | 47.8 | 59.2 | 43.9 | 70.8 | 81.9 | model | config |
| PP-YOLOE-s | 300 | 43.0 | 59.6 | 47.2 | 26.0 | 47.4 | 58.7 | 45.1 | 70.6 | 81.4 | model | config |
| PP-YOLOE-m | 300 | 49.0 | 65.9 | 53.8 | 30.9 | 53.5 | 65.3 | 50.9 | 74.4 | 84.7 | model | config |
| PP-YOLOE-l | 300 | 51.4 | 68.6 | 56.2 | 34.8 | 56.1 | 68.0 | 53.1 | 76.8 | 85.6 | model | config |
| PP-YOLOE-x | 300 | 52.3 | 69.5 | 56.8 | 35.1 | 57.0 | 68.6 | 55.5 | 76.9 | 85.7 | model | config |
Notes:
- PP-YOLOE is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset.
- 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 training, if GPU number or mini-batch size is changed, learning rate should be adjusted according to the formula lrnew = lrdefault * (batch_sizenew * GPU_numbernew) / (batch_sizedefault * GPU_numberdefault).
- 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.
Appendix
Ablation experiments of PP-YOLOE.
| NO. | Model | Box APval | Params(M) | FLOPs(G) | V100 FP32 FPS |
|---|---|---|---|---|---|
| A | PP-YOLOv2 | 49.1 | 54.58 | 115.77 | 68.9 |
| B | A + Anchor-free | 48.8 | 54.27 | 114.78 | 69.8 |
| C | B + CSPRepResNet | 49.5 | 47.42 | 101.87 | 85.5 |
| D | C + TAL | 50.4 | 48.32 | 104.75 | 84.0 |
| E | D + ET-Head | 50.9 | 52.20 | 110.07 | 78.1 |