This model is not yet complete.
| Model |
Batch |
Scale |
APval
0.5:0.95
| APval
0.5
| FLOPs
(G)
| Params
(M)
| Weight |
Los |
| RT-DETR-R18 |
4xb4 |
640 |
45.5 |
63.0 |
66.8 |
21.0 |
ckpt |
log |
| RT-DETR-R50 |
4xb4 |
640 |
50.2 |
68.5 |
113.7 |
40.4 |
ckpt |
log |
| RT-DETR-R101 |
4xb4 |
640 |
|
|
|
|
|
|
- For the backbone of the image encoder, we use the IN-1K classification pretrained weight from torchvision, which is different from the official
RT-DETR. It might be hard to train RT-DETR from scratch without IN-1K pretrained weight.
- For training, we train RT-DETR series with 6x (~72 epochs) schedule on COCO and use ModelEMA trick. We close the fp16 training trick.
- For data augmentation, we use the
color jitter, random hflip, random crop, and multi-scale training trick.
- For optimizer, we use AdamW with weight decay 0.0001 and base per image lr 0.0001 / 16.
- For learning rate scheduler, we use constant learning rate (=0.0001), following the official setting.
- For post-processing, we think it is still a little helpful to deploy NMS even if it is not essential.
Train RT-DETR
Single GPU
Taking training RT-DETR-R18 on COCO as the example,
python train.py --cuda -d coco --root path/to/coco -m rtdetr_r18 -bs 16 -size 640 --max_epoch 72 --eval_epoch 1 --ema --multi_scale
Multi GPU
Taking training RT-DETR-R18 on COCO with 4 GPUs as the example,
python -m torch.distributed.run --nproc_per_node=4 train.py --cuda -dist -d coco --root /data/datasets/ -m rtdetr_r18 -bs 16 -size 640 --max_epoch 72 --eval_epoch 1 --ema --sybn --multi_scale
Test RT-DETR
Taking testing RT-DETR-R18 on COCO-val as the example,
python test.py --cuda -d coco --root path/to/coco -m rtdetr_r18 --weight path/to/rtdetr_r18.pth -size 640 -ct 0.4 --show
Evaluate RT-DETR
Taking evaluating RT-DETR-R18 on COCO-val as the example,
python eval.py --cuda -d coco --root path/to/coco -m rtdetr_r18 --weight path/to/rtdetr_r18.pth -size 640
Demo
Detect with Image
python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m rtdetr_r18 --weight path/to/weight -size 640 -ct 0.4 --show
Detect with Video
python demo.py --mode video --path_to_vid path/to/video --cuda -m rtdetr_r18 --weight path/to/weight -size 640 -ct 0.4 --show --gif
Detect with Camera
python demo.py --mode camera --cuda -m rtdetr_r18 --weight path/to/weight -size 640 -ct 0.4 --show --gif
