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 # yolov7
-Implementation of "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors"
+
+Implementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors]()
+
+<img src="./figure/performance.png" height="480">
+
+## Performance 
+
+MS COCO
+<details><summary> <b>Expand</b> </summary>
+
+| 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 |
+| :-- | :-: | :-: | :-: | :-: | :-: | :-: |
+| **YOLOv7** | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* |
+| **YOLOv7-X** | 640 | **53.1%** | **71.2%** | **57.8%** | 114 *fps* | 4.3 *ms* |
+|  |  |  |  |  |  |  |
+| **YOLOv7-W6** | 1280 | **54.9%** | **72.6%** | **60.1%** | 84 *fps* | 7.6 *ms* |
+| **YOLOv7-E6** | 1280 | **56.0%** | **73.5%** | **61.2%** | 56 *fps* | 12.3 *ms* |
+| **YOLOv7-D6** | 1280 | **56.6%** | **74.0%** | **61.8%** | 44 *fps* | 15.0 *ms* |
+| **YOLOv7-E6E** | 1280 | **56.8%** | **74.4%** | **62.1%** | 36 *fps* | 18.7 *ms* |
+
+</details>
+
+## Installation
+
+Docker environment (recommended)
+<details><summary> <b>Expand</b> </summary>
+
+```
+# 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
+```
+
+</details>
+
+## Testing
+
+[`yolov7.pt`]()
+
+```
+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
+```
+
+## Citation
+
+```
+@article{wang2022yolov7,
+  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},
+  journal={arXiv preprint},
+  year={2022}
+}
+```
+
+## Acknowledgements
+
+<details><summary> <b>Expand</b> </summary>
+
+* [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)
+
+</details>