| Model |
Scale |
Pretrained |
FPS |
APval
0.5:0.95
| APval
0.5
| Weight |
Logs |
| PlainDETR-R50 |
800,1333 |
IN1K-Cls |
|
|
|
|
|
| PlainDETR-R50 |
800,1333 |
IN1K-MIM |
|
|
|
|
|
- We explore whether PlainDETR can still be powerful when using ResNet as the backbone.
- We set up two comparative experiments, using the ResNet-50 pre-trained for the IN1K classification task and the ResNet-50 pre-trained by IN1K's MIM as the backbone of PlainDETR. Among them, we used the MIM pre-trained ResNet-50 provided by SparK.
Train PlainDETR
Single GPU
Taking training PlainDETR on COCO as the example,
python main.py --cuda -d coco --root path/to/coco -m plain_detr_r50 --batch_size 16 --eval_epoch 2
Multi GPU
Taking training PlainDETR on COCO as the example,
python -m torch.distributed.run --nproc_per_node=8 train.py --cuda -dist -d coco --root path/to/coco -m plain_detr_r50 --batch_size 16 --eval_epoch 2
Test PlainDETR
Taking testing PlainDETR on COCO-val as the example,
python test.py --cuda -d coco --root path/to/coco -m plain_detr_r50 --weight path/to/plain_detr_r50.pth -vt 0.4 --show
Evaluate PlainDETR
Taking evaluating PlainDETR on COCO-val as the example,
python main.py --cuda -d coco --root path/to/coco -m plain_detr_r50 --resume path/to/plain_detr_r50.pth --eval_first
Demo
Detect with Image
python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m plain_detr_r50 --weight path/to/weight -vt 0.4 --show
Detect with Video
python demo.py --mode video --path_to_vid path/to/video --cuda -m plain_detr_r50 --weight path/to/weight -vt 0.4 --show --gif
Detect with Camera
python demo.py --mode camera --cuda -m plain_detr_r50 --weight path/to/weight -vt 0.4 --show --gif
