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# 模型库和评测
本页面用于汇总 MMYOLO 中支持的各类模型性能和相关评测指标，方便用户对比分析。
## COCO 数据集
<div align=center>
<img src="https://user-images.githubusercontent.com/17425982/222087414-168175cc-dae6-4c5c-a8e3-3109a152dd19.png"/>
</div>
| Model | Arch | Size | Batch Size | Epoch | SyncBN | AMP | Mem (GB) | Params(M) | FLOPs(G) | TRT-FP16-GPU-Latency(ms) | Box AP | TTA Box AP |
| :--------------: | :--: | :--: | :--------: | :---: | :----: | :-: | :------: | :-------: | :------: | :----------------------: | :----: | :--------: |
| YOLOv5-n | P5 | 640 | 8xb16 | 300 | Yes | Yes | 1.5 | 1.87 | 2.26 | 1.14 | 28.0 | 30.7 |
| YOLOv6-v2.0-n | P5 | 640 | 8xb32 | 400 | Yes | Yes | 6.04 | 4.32 | 5.52 | 1.37 | 36.2 | |
| YOLOv8-n | P5 | 640 | 8xb16 | 500 | Yes | Yes | 2.5 | 3.16 | 4.4 | 1.53 | 37.4 | 39.9 |
| RTMDet-tiny | P5 | 640 | 8xb32 | 300 | Yes | No | 11.9 | 4.90 | 8.09 | 2.31 | 41.8 | 43.2 |
| YOLOv6-v2.0-tiny | P5 | 640 | 8xb32 | 400 | Yes | Yes | 8.13 | 9.70 | 12.37 | 2.19 | 41.0 | |
| YOLOv7-tiny | P5 | 640 | 8xb16 | 300 | Yes | Yes | 2.7 | 6.23 | 6.89 | 1.88 | 37.5 | |
| YOLOX-tiny | P5 | 416 | 8xb32 | 300 | No | Yes | 4.9 | 5.06 | 7.63 | 1.19 | 34.3 | |
| RTMDet-s | P5 | 640 | 8xb32 | 300 | Yes | No | 16.3 | 8.89 | 14.84 | 2.89 | 45.7 | 47.3 |
| YOLOv5-s | P5 | 640 | 8xb16 | 300 | Yes | Yes | 2.7 | 7.24 | 8.27 | 1.89 | 37.7 | 40.2 |
| YOLOv6-v2.0-s | P5 | 640 | 8xb32 | 400 | Yes | Yes | 8.88 | 17.22 | 21.94 | 2.67 | 44.0 | |
| YOLOv8-s | P5 | 640 | 8xb16 | 500 | Yes | Yes | 4.0 | 11.17 | 14.36 | 2.61 | 45.1 | 46.8 |
| YOLOX-s | P5 | 640 | 8xb32 | 300 | No | Yes | 9.8 | 8.97 | 13.40 | 2.38 | 41.9 | |
| PPYOLOE+ -s | P5 | 640 | 8xb8 | 80 | Yes | No | 4.7 | 7.93 | 8.68 | 2.54 | 43.5 | |
| RTMDet-m | P5 | 640 | 8xb32 | 300 | Yes | No | 29.0 | 24.71 | 39.21 | 6.23 | 50.2 | 51.9 |
| YOLOv5-m | P5 | 640 | 8xb16 | 300 | Yes | Yes | 5.0 | 21.19 | 24.53 | 4.28 | 45.3 | 46.9 |
| YOLOv6-v2.0-m | P5 | 640 | 8xb32 | 300 | Yes | Yes | 16.69 | 34.25 | 40.7 | 5.12 | 48.4 | |
| YOLOv8-m | P5 | 640 | 8xb16 | 500 | Yes | Yes | 7.0 | 25.9 | 39.57 | 5.78 | 50.6 | 52.3 |
| YOLOX-m | P5 | 640 | 8xb32 | 300 | No | Yes | 17.6 | 25.33 | 36.88 | 5.31 | 47.5 | |
| PPYOLOE+ -m | P5 | 640 | 8xb8 | 80 | Yes | No | 8.4 | 23.43 | 24.97 | 5.47 | 49.5 | |
| RTMDet-l | P5 | 640 | 8xb32 | 300 | Yes | No | 45.2 | 52.32 | 80.12 | 10.13 | 52.3 | 53.7 |
| YOLOv5-l | P5 | 640 | 8xb16 | 300 | Yes | Yes | 8.1 | 46.56 | 54.65 | 6.8 | 48.8 | 49.9 |
| YOLOv6-v2.0-l | P5 | 640 | 8xb32 | 300 | Yes | Yes | 20.86 | 58.53 | 71.43 | 8.78 | 51.0 | |
| YOLOv7-l | P5 | 640 | 8xb16 | 300 | Yes | Yes | 10.3 | 36.93 | 52.42 | 6.63 | 50.9 | |
| YOLOv8-l | P5 | 640 | 8xb16 | 500 | Yes | Yes | 9.1 | 43.69 | 82.73 | 8.97 | 53.0 | 54.4 |
| YOLOX-l | P5 | 640 | 8xb8 | 300 | No | Yes | 8.0 | 54.21 | 77.83 | 9.23 | 50.1 | |
| PPYOLOE+ -l | P5 | 640 | 8xb8 | 80 | Yes | No | 13.2 | 52.20 | 55.05 | 8.2 | 52.6 | |
| RTMDet-x | P5 | 640 | 8xb32 | 300 | Yes | No | 63.4 | 94.86 | 145.41 | 17.89 | 52.8 | 54.2 |
| YOLOv7-x | P5 | 640 | 8xb16 | 300 | Yes | Yes | 13.7 | 71.35 | 95.06 | 11.63 | 52.8 | |
| YOLOv8-x | P5 | 640 | 8xb16 | 500 | Yes | Yes | 12.4 | 68.23 | 132.10 | 14.22 | 54.0 | 55.0 |
| YOLOX-x | P5 | 640 | 8xb8 | 300 | No | Yes | 9.8 | 99.07 | 144.39 | 15.35 | 51.4 | |
| PPYOLOE+ -x | P5 | 640 | 8xb8 | 80 | Yes | No | 19.1 | 98.42 | 105.48 | 14.02 | 54.2 | |
| YOLOv5-n | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 5.8 | 3.25 | 2.30 | | 35.9 | |
| YOLOv5-s | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 10.5 | 12.63 | 8.45 | | 44.4 | |
| YOLOv5-m | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 19.1 | 35.73 | 25.05 | | 51.3 | |
| YOLOv5-l | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 30.5 | 76.77 | 55.77 | | 53.7 | |
| YOLOv7-w | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 27.0 | 82.31 | 45.07 | | 54.1 | |
| YOLOv7-e | P6 | 1280 | 8xb16 | 300 | Yes | Yes | 42.5 | 114.69 | 64.48 | | 55.1 | |
- 所有模型均使用 COCO train2017 作为训练集，在 COCO val2017 上验证精度
- TRT-FP16-GPU-Latency(ms) 是指在 NVIDIA Tesla T4 设备上采用 TensorRT 8.4，batch size 为 1， 测试 shape 为 640x640 且仅包括模型 forward 的 GPU Compute time (YOLOX-tiny 测试 shape 是 416x416)
- 模型参数量和 FLOPs 是采用 [get_flops](https://github.com/open-mmlab/mmyolo/blob/dev/tools/analysis_tools/get_flops.py) 脚本得到，不同的运算方式可能略有不同
- RTMDet 性能是通过 [MMRazor 知识蒸馏](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/distillation/README.md) 训练后的结果
- MMYOLO 中暂时只实现了 YOLOv6 2.0 版本，并且 L 和 M 为没有经过知识蒸馏的结果
- YOLOv8 是引入了实例分割标注优化后的结果，YOLOv5、YOLOv6 和 YOLOv7 没有采用实例分割标注优化
- PPYOLOE+ 使用 Obj365 作为预训练权重，因此 COCO 训练的 epoch 数只需要 80
- YOLOX-tiny、YOLOX-s 和 YOLOX-m 为采用了 RTMDet 中所提优化器参数训练所得，性能相比原始实现有不同程度提升
详情见如下内容
- [RTMDet](https://github.com/open-mmlab/mmyolo/blob/main/configs/rtmdet)
- [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)
- [YOLOv6](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov6)
- [YOLOv7](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov7)
- [YOLOv8](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov8)
- [YOLOX](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolox)
- [PPYOLO-E](https://github.com/open-mmlab/mmyolo/blob/main/configs/ppyoloe)
## VOC 数据集
| Backbone | size | Batchsize | AMP | Mem (GB) | box AP(COCO metric) |
| :------: | :--: | :-------: | :-: | :------: | :-----------------: |
| YOLOv5-n | 512 | 64 | Yes | 3.5 | 51.2 |
| YOLOv5-s | 512 | 64 | Yes | 6.5 | 62.7 |
| YOLOv5-m | 512 | 64 | Yes | 12.0 | 70.1 |
| YOLOv5-l | 512 | 32 | Yes | 10.0 | 73.1 |
详情见如下内容
- [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)
## CrowdHuman 数据集
| Backbone | size | SyncBN | AMP | Mem (GB) | ignore_iof_thr | box AP50(CrowDHuman Metric) | MR | JI |
| :------: | :--: | :----: | :-: | :------: | :------------: | :-------------------------: | :--: | :---: |
| YOLOv5-s | 640 | Yes | Yes | 2.6 | -1 | 85.79 | 48.7 | 75.33 |
| YOLOv5-s | 640 | Yes | Yes | 2.6 | 0.5 | 86.17 | 48.8 | 75.87 |
详情见如下内容
- [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/main/configs/yolov5)
## DOTA 1.0 数据集
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