https://github.com/WongKinYiu/yolov7

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/WongKinYiu/yolov7

darknet pytorch scaled-yolov4 yolor yolov3 yolov4 yolov7

Last synced: 27 days ago
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Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

• Host: GitHub
• URL: https://github.com/WongKinYiu/yolov7
• Owner: WongKinYiu
• License: gpl-3.0
• Created: 2022-07-06T15:14:06.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-01T18:50:17.000Z (about 2 months ago)
• Last Synced: 2024-05-18T15:44:11.221Z (about 1 month ago)
• Topics: darknet, pytorch, scaled-yolov4, yolor, yolov3, yolov4, yolov7
• Language: Jupyter Notebook
• Homepage:
• Size: 72.5 MB
• Stars: 12,798
• Watchers: 110
• Forks: 4,035
• Open Issues: 1,499
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Lists

• awesome-stars - WongKinYiu/yolov7 - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (Jupyter Notebook)
• awesome - WongKinYiu/yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (Jupyter Notebook)
• awesome-stars - WongKinYiu/yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (Jupyter Notebook)
• awesome-yolo-object-detection - YOLOv7 - of-freebies sets new state-of-the-art for real-time object detectors". (**[CVPR 2023](https://arxiv.org/abs/2207.02696)**). "微信公众号「CVer」《[CVPR 2023 | YOLOv7强势收录！时隔6年，YOLOv系列再登CVPR！](https://mp.weixin.qq.com/s/HjaszZYPLoV03Z4Rw9KiCg)》"。 (Summary)
• awesome-stars - WongKinYiu/yolov7 - `★12952` Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (Jupyter Notebook)

README

        
# Official YOLOv7

Implementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/yolov7-trainable-bag-of-freebies-sets-new/real-time-object-detection-on-coco)](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=yolov7-trainable-bag-of-freebies-sets-new)

[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/yolov7)



[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2207.02696-B31B1B.svg)](https://arxiv.org/abs/2207.02696)



    

        

    



## Web Demo

- Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces/akhaliq/yolov7) using Gradio. Try out the Web Demo [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/yolov7)

## Performance 

MS COCO

| Model | Test Size | APtest | AP50test | AP75test | batch 1 fps | batch 32 average time |

| :-- | :-: | :-: | :-: | :-: | :-: | :-: |

| [**YOLOv7**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* |

| [**YOLOv7-X**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) | 640 | **53.1%** | **71.2%** | **57.8%** | 114 *fps* | 4.3 *ms* |

|  |  |  |  |  |  |  |

| [**YOLOv7-W6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) | 1280 | **54.9%** | **72.6%** | **60.1%** | 84 *fps* | 7.6 *ms* |

| [**YOLOv7-E6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) | 1280 | **56.0%** | **73.5%** | **61.2%** | 56 *fps* | 12.3 *ms* |

| [**YOLOv7-D6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) | 1280 | **56.6%** | **74.0%** | **61.8%** | 44 *fps* | 15.0 *ms* |

| [**YOLOv7-E6E**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) | 1280 | **56.8%** | **74.4%** | **62.1%** | 36 *fps* | 18.7 *ms* |

## Installation

Docker environment (recommended)

 Expand 

``` shell

# create the docker container, you can change the share memory size if you have more.

nvidia-docker run --name yolov7 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3

# apt install required packages

apt update

apt install -y zip htop screen libgl1-mesa-glx

# pip install required packages

pip install seaborn thop

# go to code folder

cd /yolov7

```

## Testing

[`yolov7.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) [`yolov7x.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) [`yolov7-w6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) [`yolov7-e6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) [`yolov7-d6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) [`yolov7-e6e.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt)

``` shell

python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val

```

You will get the results:

```

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.51206

 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.69730

 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.55521

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35247

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55937

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66693

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.38453

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.63765

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.68772

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.53766

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73549

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868

```

To measure accuracy, download [COCO-annotations for Pycocotools](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) to the `./coco/annotations/instances_val2017.json`

## Training

Data preparation

``` shell

bash scripts/get_coco.sh

```

* Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip). If you have previously used a different version of YOLO, we strongly recommend that you delete `train2017.cache` and `val2017.cache` files, and redownload [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip) 

Single GPU training

``` shell

# train p5 models

python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml

# train p6 models

python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml

```

Multiple GPU training

``` shell

# train p5 models

python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml

# train p6 models

python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_aux.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch-size 128 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml

```

## Transfer learning

[`yolov7_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt) [`yolov7x_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x_training.pt) [`yolov7-w6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6_training.pt) [`yolov7-e6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6_training.pt) [`yolov7-d6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6_training.pt) [`yolov7-e6e_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e_training.pt)

Single GPU finetuning for custom dataset

``` shell

# finetune p5 models

python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml

# finetune p6 models

python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/custom.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6-custom.yaml --weights 'yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml

```

## Re-parameterization

See [reparameterization.ipynb](tools/reparameterization.ipynb)

## Inference

On video:

``` shell

python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4

```

On image:

``` shell

python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg

```



    

        

    



## Export

**Pytorch to CoreML (and inference on MacOS/iOS)** 

**Pytorch to ONNX with NMS (and inference)** 

```shell

python export.py --weights yolov7-tiny.pt --grid --end2end --simplify \

        --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640

```

**Pytorch to TensorRT with NMS (and inference)** 

```shell

wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt

python export.py --weights ./yolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640

git clone https://github.com/Linaom1214/tensorrt-python.git

python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16

```

**Pytorch to TensorRT another way**   Expand 

```shell

wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt

python export.py --weights yolov7-tiny.pt --grid --include-nms

git clone https://github.com/Linaom1214/tensorrt-python.git

python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16

# Or use trtexec to convert ONNX to TensorRT engine

/usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16

```

Tested with: Python 3.7.13, Pytorch 1.12.0+cu113

## Pose estimation

[`code`](https://github.com/WongKinYiu/yolov7/tree/pose) [`yolov7-w6-pose.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6-pose.pt)

See [keypoint.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/keypoint.ipynb).



    

        

    



## Instance segmentation (with NTU)

[`code`](https://github.com/WongKinYiu/yolov7/tree/mask) [`yolov7-mask.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-mask.pt)

See [instance.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/instance.ipynb).



    

        

    



## Instance segmentation

[`code`](https://github.com/WongKinYiu/yolov7/tree/u7/seg) [`yolov7-seg.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-seg.pt)

YOLOv7 for instance segmentation (YOLOR + YOLOv5 + YOLACT)

| Model | Test Size | APbox | AP50box | AP75box | APmask | AP50mask | AP75mask |

| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: |

| **YOLOv7-seg** | 640 | **51.4%** | **69.4%** | **55.8%** | **41.5%** | **65.5%** | **43.7%** |

## Anchor free detection head

[`code`](https://github.com/WongKinYiu/yolov7/tree/u6) [`yolov7-u6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-u6.pt)

YOLOv7 with decoupled TAL head (YOLOR + YOLOv5 + YOLOv6)

| Model | Test Size | APval | AP50val | AP75val |

| :-- | :-: | :-: | :-: | :-: |

| **YOLOv7-u6** | 640 | **52.6%** | **69.7%** | **57.3%** |

## Citation

```

@inproceedings{wang2023yolov7,

  title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},

  author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},

  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

  year={2023}

}

```

```

@article{wang2023designing,

  title={Designing Network Design Strategies Through Gradient Path Analysis},

  author={Wang, Chien-Yao and Liao, Hong-Yuan Mark and Yeh, I-Hau},

  journal={Journal of Information Science and Engineering},

  year={2023}

}

```

## Teaser

YOLOv7-semantic & YOLOv7-panoptic & YOLOv7-caption



    

        

    

    

        

    

    

        

    

    

        

    



YOLOv7-semantic & YOLOv7-detection & YOLOv7-depth (with NTUT)



    

        

    



YOLOv7-3d-detection & YOLOv7-lidar & YOLOv7-road (with NTUT)



    

        

    

    

        

    

    

        

    



## Acknowledgements

 Expand 

* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)

* [https://github.com/WongKinYiu/yolor](https://github.com/WongKinYiu/yolor)

* [https://github.com/WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4)

* [https://github.com/WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)

* [https://github.com/Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)

* [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3)

* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)

* [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)

* [https://github.com/JUGGHM/OREPA_CVPR2022](https://github.com/JUGGHM/OREPA_CVPR2022)

* [https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose](https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose)