| Model | Size | Self-Distill | Pre-Train Backbone |
mAPval 0.5:0.95 |
Speed (fps) T4-TRT7-FP16 bs1 / bs32 |
Speed (fps) T4-TRT8-FP16 bs1 / bs32 |
Params (M) |
FLOPs (G) |
Weight |
|---|---|---|---|---|---|---|---|---|---|
| Gold-YOLO-N | 640 | ✅ | ❌ | 39.9 | 563 / 1030 | 657 / 1191 | 5.6 | 12.1 | Google Drive cowtransfer |
| Gold-YOLO-S | 640 | ✅ | ✅ | 46.4 | 286 / 446 | 308 / 492 | 21.5 | 46.0 | Google Drive cowtransfer |
| Gold-YOLO-M | 640 | ✅ | ✅ | 51.1 | 152 / 220 | 157 / 241 | 41.3 | 87.5 | Google Drive cowtransfer |
| Gold-YOLO-L | 640 | ✅ | ✅ | 53.3 | 88 / 116 | 94 / 137 | 75.1 | 151.7 | Google Drive cowtransfer |
Table Notes
- Results of the mAP and speed are evaluated on COCO val2017 dataset with the input resolution of 640×640.
- Speed is tested with TensorRT 7.2 and TensorRT 8.5 on T4 GPU.
-
python requirements
pip install -r requirements.txt
-
data:
prepare COCO dataset, YOLO format coco labels and specify dataset paths in data.yaml
-
Step 1: Training a base model
Be sure to open use_dfl mode in config file (use_dfl=True, reg_max=16)
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \ --batch 128 \ --conf configs/gold_yolo-n.py \ --data data/coco.yaml \ --epoch 300 \ --fuse_ab \ --use_syncbn \ --device 0,1,2,3,4,5,6,7 \ --name gold_yolo-n
-
Step 2: Self-distillation training
Be sure to open use_dfl mode in config file (use_dfl=True, reg_max=16)
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \ --batch 128 \ --conf configs/gold_yolo-n.py \ --data data/coco.yaml \ --epoch 300 \ --device 0,1,2,3,4,5,6,7 \ --use_syncbn \ --distill \ --teacher_model_path runs/train/gold_yolo_n/weights/best_ckpt.pt \ --name gold_yolo-n
-
Step 1: Training a base model
Be sure to open use_dfl mode in config file (use_dfl=True, reg_max=16)
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \ --batch 256 \ --conf configs/gold_yolo-s.py \ # gold_yolo-m/gold_yolo-l --data data/coco.yaml \ --epoch 300 \ --fuse_ab \ --use_syncbn \ --device 0,1,2,3,4,5,6,7 \ --name gold_yolo-s # gold_yolo-m/gold_yolo-l
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Step 2: Self-distillation training
Be sure to open use_dfl mode in config file (use_dfl=True, reg_max=16)
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \ --batch 256 \ # 128 for distillation of gold_yolo-l --conf configs/gold_yolo-s.py \ # gold_yolo-m/gold_yolo-l --data data/coco.yaml \ --epoch 300 \ --device 0,1,2,3,4,5,6,7 \ --use_syncbn \ --distill \ --teacher_model_path runs/train/gold_yolo-s/weights/best_ckpt.pt \ --name gold_yolo-s # gold_yolo-m/gold_yolo-l
python tools/eval.py --data data/coco.yaml --batch 32 --weights weights/Gold_s_pre_dist.pt --task val --reproduce_640_evalPlease refer to Test speed
The implementation is based on YOLOv6, and some implementations borrowed from Topformer. Thanks for their open source code.