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Kin-Yiu, Wong
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README.md
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README.md
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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)
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[](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=yolov7-trainable-bag-of-freebies-sets-new)
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[](https://huggingface.co/spaces/akhaliq/yolov7)
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<a href="https://colab.research.google.com/gist/AlexeyAB/b769f5795e65fdab80086f6cb7940dae/yolov7detection.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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[](https://arxiv.org/abs/2207.02696)
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<div align="center">
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<a href="./">
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<img src="./figure/performance.png" width="79%"/>
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</a>
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</div>
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## Web Demo
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- Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces/akhaliq/yolov7) using Gradio. Try out the Web Demo [](https://huggingface.co/spaces/akhaliq/yolov7)
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## Performance
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MS COCO
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| Model | Test Size | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | batch 1 fps | batch 32 average time |
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| :-- | :-: | :-: | :-: | :-: | :-: | :-: |
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| [**YOLOv7**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* |
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| [**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* |
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| | | | | | | |
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| [**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* |
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| [**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* |
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| [**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* |
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| [**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* |
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## Installation
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Docker environment (recommended)
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<details><summary> <b>Expand</b> </summary>
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``` shell
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# create the docker container, you can change the share memory size if you have more.
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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
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# apt install required packages
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apt update
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apt install -y zip htop screen libgl1-mesa-glx
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# pip install required packages
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pip install seaborn thop
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# go to code folder
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cd /yolov7
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```
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</details>
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Instance segmentaion code is partially based on [BlendMask](https://arxiv.org/abs/2001.00309).
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## Testing
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[`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)
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[yolov7-mask.pt](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-mask.pt)
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``` shell
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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
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```
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You will get the results:
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```
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.51206
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.69730
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.55521
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35247
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55937
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66693
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.38453
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.63765
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.68772
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.53766
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73549
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868
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```
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To measure accuracy, download [COCO-annotations for Pycocotools](http://images.cocodataset.org/annotations/annotations_trainval2017.zip).
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## Training
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Data preparation
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``` shell
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bash scripts/get_coco.sh
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```
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* 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)
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Single GPU training
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``` shell
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# train p5 models
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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
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# train p6 models
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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
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```
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Multiple GPU training
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``` shell
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# train p5 models
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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
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# train p6 models
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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
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```
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## Transfer learning
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[`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)
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Single GPU finetuning for custom dataset
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``` shell
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# finetune p5 models
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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
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# finetune p6 models
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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
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```
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## Re-parameterization
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See [reparameterization.ipynb](tools/reparameterization.ipynb)
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## Pose estimation
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[`yolov7-w6-pose.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6-pose.pt)
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See [keypoint.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/keypoint.ipynb).
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## Inference
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On video:
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``` shell
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python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4
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```
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On image:
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``` shell
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python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg
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```
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[[scripts]](./tools/instance.ipynb)
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<div align="center">
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<a href="./">
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<img src="./figure/horses_prediction.jpg" width="59%"/>
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<img src="./figure/horses_instance.png" width="79%"/>
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</a>
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</div>
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## Export
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**Pytorch to ONNX with NMS (and inference)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7onnx.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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```shell
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python export.py --weights yolov7-tiny.pt --grid --end2end --simplify \
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--topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640
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```
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**Pytorch to TensorRT with NMS (and inference)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7trt.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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```shell
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wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
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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
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git clone https://github.com/Linaom1214/tensorrt-python.git
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python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16
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```
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**Pytorch to TensorRT another way** <a href="https://colab.research.google.com/gist/AlexeyAB/fcb47ae544cf284eb24d8ad8e880d45c/yolov7trtlinaom.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <details><summary> <b>Expand</b> </summary>
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```shell
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wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
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python export.py --weights yolov7-tiny.pt --grid --include-nms
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git clone https://github.com/Linaom1214/tensorrt-python.git
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python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16
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# Or use trtexec to convert ONNX to TensorRT engine
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/usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16
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```
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</details>
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Tested with: Python 3.7.13, Pytorch 1.12.0+cu113
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## Citation
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```
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@ -200,20 +27,6 @@ Tested with: Python 3.7.13, Pytorch 1.12.0+cu113
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}
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```
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## Teaser
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Yolov7-mask & YOLOv7-pose
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<div align="center">
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<a href="./">
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<img src="./figure/mask.png" width="56%"/>
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</a>
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<a href="./">
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<img src="./figure/pose.png" width="42%"/>
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</a>
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</div>
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## Acknowledgements
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<details><summary> <b>Expand</b> </summary>
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