| Model |
Backbone |
Batch |
Scale |
APval
0.5:0.95
| APval
0.5
| FLOPs
(G)
| Params
(M)
| Weight |
| YOLOv4-Tiny |
CSPDarkNet-Tiny |
1xb16 |
640 |
31.0 |
49.1 |
8.1 |
2.9 |
ckpt |
| YOLOv4 |
CSPDarkNet-53 |
1xb16 |
640 |
46.6 |
65.8 |
162.7 |
61.5 |
ckpt |
- For training, we train YOLOv4 and YOLOv4-Tiny with 250 epochs on COCO.
- For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation, following the setting of YOLOv5.
- For optimizer, we use SGD with momentum 0.937, weight decay 0.0005 and base lr 0.01.
- For learning rate scheduler, we use linear decay scheduler.
- For YOLOv4's structure, we use decoupled head, following the setting of YOLOX.
Train YOLOv4
Single GPU
Taking training YOLOv4 on COCO as the example,
python train.py --cuda -d coco --root path/to/coco -m yolov4 -bs 16 -size 640 --wp_epoch 3 --max_epoch 300 --eval_epoch 10 --no_aug_epoch 20 --ema --fp16 --multi_scale
Multi GPU
Taking training YOLOv4 on COCO as the example,
python -m torch.distributed.run --nproc_per_node=8 train.py --cuda -dist -d coco --root /data/datasets/ -m yolov4 -bs 128 -size 640 --wp_epoch 3 --max_epoch 300 --eval_epoch 10 --no_aug_epoch 20 --ema --fp16 --sybn --multi_scale --save_folder weights/
Test YOLOv4
Taking testing YOLOv4 on COCO-val as the example,
python test.py --cuda -d coco --root path/to/coco -m yolov4 --weight path/to/yolov4_coco.pth -size 640 --show
Evaluate YOLOv4
Taking evaluating YOLOv4 on COCO-val as the example,
python eval.py --cuda -d coco --root path/to/coco -m yolov4 --weight path/to/yolov4_coco.pth
Demo
Detect with Image
python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m yolov4 --weight path/to/yolov4_coco.pth -size 640 --show
Detect with Video
python demo.py --mode video --path_to_vid path/to/video --cuda -m yolov4 --weight path/to/yolov4_coco.pth -size 640 --show --gif
Detect with Camera
python demo.py --mode camera --cuda -m yolov4 --weight path/to/yolov4_coco.pth -size 640 --show --gif
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