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Chilicyy committed Jan 6, 2023
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Expand Up @@ -18,26 +18,46 @@ English | [简体中文](README_cn.md)
Implementation of paper - [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://arxiv.org/abs/2209.02976)

<p align="center">
<img src="assets/speed_comparision_v2.png" align="middle" width = "1000" />
<img src="assets/speed_comparision_v3.png" align="middle" width = "1000" />
</p>

YOLOv6 has a series of models for various industrial scenarios, including N/T/S/M/L, which the architectures vary considering the model size for better accuracy-speed trade-off. And some Bag-of-freebies methods are introduced to further improve the performance, such as self-distillation and more training epochs. For industrial deployment, we adopt QAT with channel-wise distillation and graph optimization to pursue extreme performance.

YOLOv6-N hits 35.9% AP on COCO dataset with 1234 FPS on T4. YOLOv6-S strikes 43.5% AP with 495 FPS, and the quantized YOLOv6-S model achieves 43.3% AP at a accelerated speed of 869 FPS on T4. YOLOv6-T/M/L also have excellent performance, which show higher accuracy than other detectors with the similar inference speed.


## What's New

- [2023.01.06] Release P6 models and enhance the performance of P5 models. ⭐️ [Benchmark](#Benchmark)
- [2022.11.04] Release [base models](configs/base/README.md) to simplify the training and deployment process.
- [2022.09.06] Customized quantization methods. 🚀 [Quantization Tutorial](./tools/qat/README.md)
- [2022.09.05] Release M/L models and update N/T/S models with enhanced performance.⭐️ [Benchmark](#Benchmark)
- [2022.09.05] Release M/L models and update N/T/S models with enhanced performance.
- [2022.06.23] Release N/T/S models with excellent performance.

## Benchmark
| Model | Size | mAP<sup>val<br/>0.5:0.95 | Speed<sup>T4<br/>trt fp16 b1 <br/>(fps) | Speed<sup>T4<br/>trt fp16 b32 <br/>(fps) | Params<br/><sup> (M) | FLOPs<br/><sup> (G) |
| :----------------------------------------------------------- | ---- | :----------------------- | --------------------------------------- | ---------------------------------------- | -------------------- | ------------------- |
| [**YOLOv6-N**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6n.pt) | 640 | 37.5 | 779 | 1187 | 4.7 | 11.4 |
| [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6s.pt) | 640 | 45.0 | 339 | 484 | 18.5 | 45.3 |
| [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6m.pt) | 640 | 50.0 | 175 | 226 | 34.9 | 85.8 |
| [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6l.pt) | 640 | 52.8 | 98 | 116 | 59.6 | 150.7 |
| | | | | |
| [**YOLOv6-N6**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6n6.pt) | 1280 | 44.9 | 228 | 281 | 10.4 | 49.8 |
| [**YOLOv6-S6**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6s6.pt) | 1280 | 50.3 | 98 | 108 | 41.4 | 198.0 |
| [**YOLOv6-M6**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6m6.pt) | 1280 | 55.2 | 47 | 55 | 79.6 | 379.5 |
| [**YOLOv6-L6**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6l6.pt) | 1280 | 57.2 | 26 | 29 | 140.4 | 673.4 |
<details>
<summary>Table Notes</summary>
- All checkpoints are trained with self-distillation except for YOLOv6-N6/S6 models trained to 300 epochs without distillation.
- Results of the mAP and speed are evaluated on [COCO val2017](https://cocodataset.org/#download) dataset with the input resolution of 640×640 for P5 models and 1280x1280 for P6 models.
- Speed is tested with TensorRT 7.2 on T4.
- Refer to [Test speed](./docs/Test_speed.md) tutorial to reproduce the speed results of YOLOv6.
- Params and FLOPs of YOLOv6 are estimated on deployed models.
</details>

<details>
<summary>Legacy models</summary>

| Model | Size | mAP<sup>val<br/>0.5:0.95 | Speed<sup>T4<br/>trt fp16 b1 <br/>(fps) | Speed<sup>T4<br/>trt fp16 b32 <br/>(fps) | Params<br/><sup> (M) | FLOPs<br/><sup> (G) |
| :----------------------------------------------------------- | ---- | :------------------------------------ | --------------------------------------- | ---------------------------------------- | -------------------- | ------------------- |
| [**YOLOv6-N**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6n.pt) | 640 | 35.9<sup>300e</sup><br/>36.3<sup>400e | 802 | 1234 | 4.3 | 11.1 |
| [**YOLOv6-T**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6t.pt) | 640 | 40.3<sup>300e</sup><br/>41.1<sup>400e | 449 | 659 | 9.7 | 24.9 |
| [**YOLOv6-T**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6t.pt) | 640 | 40.3<sup>300e</sup><br/>41.1<sup>400e | 449 | 659 | 15.0 | 36.7 |
| [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6s.pt) | 640 | 43.5<sup>300e</sup><br/>43.8<sup>400e | 358 | 495 | 17.2 | 44.2 |
| [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6m.pt) | 640 | 49.5 | 179 | 233 | 34.3 | 82.2 |
| [**YOLOv6-L-ReLU**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6l_relu.pt) | 640 | 51.7 | 113 | 149 | 58.5 | 144.0 |
Expand All @@ -56,19 +76,6 @@ YOLOv6-N hits 35.9% AP on COCO dataset with 1234 FPS on T4. YOLOv6-S strikes 43.

- Speed is tested with TensorRT 8.4 on T4.
- Precision is figured on models for 300 epochs.
- Results of the mAP and speed are evaluated on [COCO val2017](https://cocodataset.org/#download) dataset with the input resolution of 640×640.
- Refer to [Test speed](./docs/Test_speed.md) tutorial to reproduce the speed results of YOLOv6.
- Params and FLOPs of YOLOv6 are estimated on deployed models.

<details>
<summary>Legacy models</summary>

| Model | Size | mAP<sup>val<br/>0.5:0.95 | Speed<sup>V100<br/>fp16 b32 <br/>(ms) | Speed<sup>V100<br/>fp32 b32 <br/>(ms) | Speed<sup>T4<br/>trt fp16 b1 <br/>(fps) | Speed<sup>T4<br/>trt fp16 b32 <br/>(fps) | Params<br/><sup> (M) | FLOPs<br/><sup> (G) |
| :-------------- | ----------- | :----------------------- | :------------------------------------ | :------------------------------------ | ---------------------------------------- | ----------------------------------------- | --------------- | -------------- |
| [**YOLOv6-N**](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6n.pt) | 416<br/>640 | 30.8<br/>35.0 | 0.3<br/>0.5 | 0.4<br/>0.7 | 1100<br/>788 | 2716<br/>1242 | 4.3<br/>4.3 | 4.7<br/>11.1 |
| [**YOLOv6-T**](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6t.pt) | 640 | 41.3 | 0.9 | 1.5 | 425 | 602 | 15.0 | 36.7 |
| [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6s.pt) | 640 | 43.1 | 1.0 | 1.7 | 373 | 520 | 17.2 | 44.2 |


</details>

Expand All @@ -88,21 +95,28 @@ pip install -r requirements.txt



<details open>
<details>
<summary> Training</summary>


Single GPU

```shell
python tools/train.py --batch 32 --conf configs/yolov6s_finetune.py --data data/dataset.yaml --device 0
# P5 models
python tools/train.py --batch 32 --conf configs/yolov6s_finetune.py --data data/dataset.yaml --fuse_ab --device 0
# P6 models
python tools/train.py --batch 32 --conf configs/yolov6s6_finetune.py --data data/dataset.yaml --img 1280 --device 0
```

Multi GPUs (DDP mode recommended)

```shell
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 256 --conf configs/yolov6s_finetune.py --data data/dataset.yaml --device 0,1,2,3,4,5,6,7
# P5 models
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 256 --conf configs/yolov6s_finetune.py --data data/dataset.yaml --fuse_ab --device 0,1,2,3,4,5,6,7
# P6 models
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 128 --conf configs/yolov6s6_finetune.py --data data/dataset.yaml --img 1280 --device 0,1,2,3,4,5,6,7
```
- fuse_ab: add anchor-based auxiliary branch and use Anchor Unified Training Mode(Not supported on P6 models)
- conf: select config file to specify network/optimizer/hyperparameters. We recommend to apply yolov6n/s/m/l_finetune.py when training on your custom dataset.
- data: prepare [COCO](http://cocodataset.org) dataset, [YOLO format coco labels](https://github.com/meituan/YOLOv6/releases/download/0.1.0/coco2017labels.zip) and specify dataset paths in data.yaml
- make sure your dataset structure as follows:
Expand All @@ -122,57 +136,7 @@ python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 256
```



<details>
<summary>Reproduce our results on COCO ⭐️</summary>

For nano model
```shell
python -m torch.distributed.launch --nproc_per_node 4 tools/train.py \
--batch 128 \
--conf configs/yolov6n.py \
--data data/coco.yaml \
--epoch 400 \
--device 0,1,2,3 \
--name yolov6n_coco
```

For s/tiny model
```shell
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \
--batch 256 \
--conf configs/yolov6s.py \ # configs/yolov6t.py
--data data/coco.yaml \
--epoch 400 \
--device 0,1,2,3,4,5,6,7 \
--name yolov6s_coco # yolov6t_coco
```

For m/l model
```shell
# Step 1: Training a base model
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \
--batch 256 \
--conf configs/yolov6m.py \ # configs/yolov6l.py
--data data/coco.yaml \
--epoch 300 \
--device 0,1,2,3,4,5,6,7 \
--name yolov6m_coco # yolov6l_coco


# Step 2: Self-distillation training
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \
--batch 256 \ # 128 for distillation of yolov6l
--conf configs/yolov6m.py \ # configs/yolov6l.py
--data data/coco.yaml \
--epoch 300 \
--device 0,1,2,3,4,5,6,7 \
--distill \
--teacher_model_path runs/train/yolov6m_coco/weights/best_ckpt.pt \ # # yolov6l_coco
--name yolov6m_coco # yolov6l_coco

```
</details>
Reproduce our results on COCO ⭐️ [Train COCO Dataset](./docs/Train_coco_data.md)

<details>
<summary>Resume training</summary>
Expand Down Expand Up @@ -200,10 +164,13 @@ This will resume from the specific checkpoint you provide.
<details>
<summary> Evaluation</summary>

Reproduce mAP on COCO val2017 dataset with 640×640 resolution ⭐️
Reproduce mAP on COCO val2017 dataset with 640×640 or 1280x1280 resolution ⭐️

```shell
# P5 models
python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s.pt --task val --reproduce_640_eval
# P6 models
python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s6.pt --task val --reproduce_640_eval --img 1280
```
- verbose: set True to print mAP of each classes.
- do_coco_metric: set True / False to enable / disable pycocotools evaluation method.
Expand All @@ -215,12 +182,15 @@ python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s.pt --tas
<details>
<summary>Inference</summary>

First, download a pretrained model from the YOLOv6 [release](https://github.com/meituan/YOLOv6/releases/tag/0.2.0) or use your trained model to do inference.
First, download a pretrained model from the YOLOv6 [release](https://github.com/meituan/YOLOv6/releases/tag/0.3.0) or use your trained model to do inference.

Second, run inference with `tools/infer.py`

```shell
# P5 models
python tools/infer.py --weights yolov6s.pt --source img.jpg / imgdir / video.mp4
# P6 models
python tools/infer.py --weights yolov6s6.pt --img 1280 --source img.jpg / imgdir / video.mp4
```
</details>

Expand All @@ -236,6 +206,7 @@ python tools/infer.py --weights yolov6s.pt --source img.jpg / imgdir / video.mp4
<details open>
<summary> Tutorials</summary>

* [Train COCO Dataset](./docs/Train_coco_data.md)
* [Train custom data](./docs/Train_custom_data.md)
* [Test speed](./docs/Test_speed.md)
* [Tutorial of Quantization for YOLOv6](./docs/Tutorial%20of%20Quantization.md)
Expand Down
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