| Model |
Batch |
Scale |
APval
0.5:0.95
| APval
0.5
| FLOPs
(G)
| Params
(M)
| Weight |
| YOLOX-S |
8xb8 |
640 |
40.1 |
60.3 |
26.8 |
8.9 |
ckpt |
| YOLOX-M |
8xb8 |
640 |
46.2 |
66.0 |
74.3 |
25.4 |
ckpt |
| YOLOX-L |
8xb8 |
640 |
48.7 |
68.0 |
155.4 |
54.2 |
ckpt |
| YOLOX-X |
8xb8 |
640 |
|
|
|
|
|
- For training, we train YOLOX series with 300 epochs on COCO.
- For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation.
- For optimizer, we use SGD with weight decay 0.0005 and base per image lr 0.01 / 64,.
- For learning rate scheduler, we use Cosine decay scheduler.
Train YOLOX
Single GPU
Taking training YOLOX-S on COCO as the example,
python train.py --cuda -d coco --root path/to/coco -m yolox_s -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 YOLOX-S on COCO as the example,
python -m torch.distributed.run --nproc_per_node=8 train.py --cuda -dist -d coco --root /data/datasets/ -m yolox_s -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 YOLOX
Taking testing YOLOX-S on COCO-val as the example,
python test.py --cuda -d coco --root path/to/coco -m yolox_s --weight path/to/yolox_s.pth -size 640 -vt 0.4 --show
Evaluate YOLOX
Taking evaluating YOLOX-S on COCO-val as the example,
python eval.py --cuda -d coco-val --root path/to/coco -m yolox_s --weight path/to/yolox_s.pth
Demo
Detect with Image
python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m yolox_s --weight path/to/weight -size 640 -vt 0.4 --show
Detect with Video
python demo.py --mode video --path_to_vid path/to/video --cuda -m yolox_s --weight path/to/weight -size 640 -vt 0.4 --show --gif
Detect with Camera
python demo.py --mode camera --cuda -m yolox_s --weight path/to/weight -size 640 -vt 0.4 --show --gif
yjh0410
5183498321
release YOLOv8-s