| Model |
Scale |
Batch |
APval
0.5:0.95
| APval
0.5
| FLOPs
(G)
| Params
(M)
| Weight |
| RTCDet-P |
320 |
8xb16 |
|
|
|
|
- |
| RTCDet-P |
416 |
8xb16 |
|
|
|
|
- |
| RTCDet-P |
512 |
8xb16 |
|
|
|
|
- |
| RTCDet-P |
640 |
8xb16 |
|
|
|
|
- |
- For training, we train my RTCDet series series with 300 epochs on COCO.
- For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation, following the setting of YOLOX, but we remove the rotation transformation which is used in YOLOX's strong augmentation.
- For optimizer, we use AdamW with weight decay 0.05 and base per image lr 0.001 / 64.
- For learning rate scheduler, we use linear decay scheduler.
- Due to my limited computing resources, I can not train
RTCDet-X with the setting of batch size=128.
Train RTCDet
Single GPU
Taking training RTCDet-S on COCO as the example,
python train.py --cuda -d coco --root path/to/coco -m rtcdet_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 RTCDet-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 rtcdet_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 RTCDet
Taking testing RTCDet-S on COCO-val as the example,
python test.py --cuda -d coco --root path/to/coco -m rtcdet_s --weight path/to/rtcdet_s.pth -size 640 -vt 0.4 --show
Evaluate RTCDet
Taking evaluating RTCDet-S on COCO-val as the example,
python eval.py --cuda -d coco-val --root path/to/coco -m rtcdet_s --weight path/to/rtcdet_s.pth
Demo
Detect with Image
python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m rtcdet_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 rtcdet_s --weight path/to/weight -size 640 -vt 0.4 --show --gif
Detect with Camera
python demo.py --mode camera --cuda -m rtcdet_s --weight path/to/weight -size 640 -vt 0.4 --show --gif
