RT-ODLab/models/detectors/rtcdet

RTCDet:

• For training, we train RTCDet series with 500 epochs on COCO.
• For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation, following the setting of RTCDet.
• For optimizer, we use AdamW with weight decay 0.05 and base per image lr 0.001 / 64, which is different from the official RTCDet. We have tried SGD, but it has weakened performance. For example, when using SGD, RTCDet-N's AP was only 35.8%, lower than the current result (36.8 %), perhaps because some hyperparameters were not set properly.
• For learning rate scheduler, we use linear decay scheduler.

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 500 --eval_epoch 10 --no_aug_epoch 20 --ema --fp16 --multi_scale 

Multi GPU

Taking training RTCDet 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 500  --eval_epoch 10 --no_aug_epoch 20 --ema --fp16 --sybn --multi_scale --save_folder weights/ 

Test RTCDet

Taking testing RTCDet on COCO-val as the example,

python test.py --cuda -d coco --root path/to/coco -m rtcdet_s --weight path/to/RTCDet.pth -size 640 -vt 0.4 --show 

Evaluate RTCDet

Taking evaluating RTCDet on COCO-val as the example,

python eval.py --cuda -d coco-val --root path/to/coco -m rtcdet_s --weight path/to/RTCDet.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

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Model	Batch	Scale	APval 0.5:0.95	APval 0.5	FLOPs (G)	Params (M)	Weight
RTCDet-N	8xb16	640
RTCDet-S	8xb16	640
RTCDet-M	8xb16	640
RTCDet-L	8xb16	640