yolov7

models/yolov7_af/README.md

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+# Anchor-free YOLOv7:
+
+- VOC
+
+|     Model   | Batch | Scale | AP<sup>val<br>0.5 | Weight |  Logs  |
+|-------------|-------|-------|-------------------|--------|--------|
+| YOLOv7-AF-S | 1xb16 |  640  |       82.7        | [ckpt](https://github.com/yjh0410/YOLO-Tutorial-v7/releases/download/yolo_tutorial_ckpt/yolov7_af_s_voc.pth) | [log](https://github.com/yjh0410/YOLO-Tutorial-v7/releases/download/yolo_tutorial_ckpt/YOLOv7-AF-S-VOC.txt) |
+
+- COCO
+
+|    Model    | Batch | Scale | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |  Logs  |
+|-------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|--------|
+| YOLOv7-AF-S | 1xb16 |  640  |                    |               |   26.9            |   8.9             |  |  |
+
+- For training, we train redesigned YOLOv7-AF with 500 epochs on COCO. We also use the gradient accumulation.
+- For data augmentation, we use the RandomAffine, RandomHSV, Mosaic and YOLOX's Mixup augmentation.
+- For optimizer, we use AdamW with weight decay of 0.05 and per image base lr of 0.001 / 64.
+- For learning rate scheduler, we use cosine decay scheduler.
+- For batch size, we set it to 16, and we also use the gradient accumulation to approximate batch size of 256.
+
+
+## Train YOLOv7-AF
+### Single GPU
+Taking training YOLOv7-AF-S on COCO as the example,
+```Shell
+python train.py --cuda -d coco --root path/to/coco -m yolov7_af_s -bs 16 --fp16 
+```
+
+### Multi GPU
+Taking training YOLOv7-AF-S on COCO as the example,
+```Shell
+python -m torch.distributed.run --nproc_per_node=8 train.py --cuda --distributed -d coco --root path/to/coco -m yolov7_af_s -bs 16 --fp16 
+```
+
+## Test YOLOv7-AF
+Taking testing YOLOv7-AF-S on COCO-val as the example,
+```Shell
+python test.py --cuda -d coco --root path/to/coco -m yolov7_af_s --weight path/to/yolov7.pth --show 
+```
+
+## Evaluate YOLOv7-AF
+Taking evaluating YOLOv7-AF-S on COCO-val as the example,
+```Shell
+python eval.py --cuda -d coco --root path/to/coco -m yolov7_af_s --weight path/to/yolov7.pth 
+```
+
+## Demo
+### Detect with Image
+```Shell
+python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m yolov7_af_s --weight path/to/weight --show
+```
+
+### Detect with Video
+```Shell
+python demo.py --mode video --path_to_vid path/to/video --cuda -m yolov7_af_s --weight path/to/weight --show --gif
+```
+
+### Detect with Camera
+```Shell
+python demo.py --mode camera --cuda -m yolov7_af_s --weight path/to/weight --show --gif
+```