NanoDet-Plus v1.0.0-alpha
NanoDet-Plus v1.0.0-alpha
In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training. We also introduce a light feature pyramid called Ghost-PAN to enhance multi-layer feature fusion. These improvements boost previous NanoDet's detection accuracy by 7 mAP on COCO dataset.
| Model | Resolution | mAPval 0.5:0.95 |
CPU Latency (i7-8700) |
ARM Latency (4xA76) |
FLOPS | Params | Model Size |
|---|---|---|---|---|---|---|---|
| NanoDet-m | 320*320 | 20.6 | 4.98ms | 10.23ms | 0.72G | 0.95M | 1.8MB(FP16) | 980KB(INT8) |
| NanoDet-Plus-m | 320*320 | 27.0 | 5.25ms | 11.97ms | 0.9G | 1.17M | 2.3MB(FP16) | 1.2MB(INT8) |
| NanoDet-Plus-m | 416*416 | 30.4 | 8.32ms | 19.77ms | 1.52G | 1.17M | 2.3MB(FP16) | 1.2MB(INT8) |
| NanoDet-Plus-m-1.5x | 320*320 | 29.9 | 7.21ms | 15.90ms | 1.75G | 2.44M | 4.7MB(FP16) | 2.3MB(INT8) |
| NanoDet-Plus-m-1.5x | 416*416 | 34.1 | 11.50ms | 25.49ms | 2.97G | 2.44M | 4.7MB(FP16) | 2.3MB(INT8) |
| YOLOv3-Tiny | 416*416 | 16.6 | - | 37.6ms | 5.62G | 8.86M | 33.7MB |
| YOLOv4-Tiny | 416*416 | 21.7 | - | 32.81ms | 6.96G | 6.06M | 23.0MB |
| YOLOX-Nano | 416*416 | 25.8 | - | 23.08ms | 1.08G | 0.91M | 1.8MB(FP16) |
| YOLOv5-n | 640*640 | 28.4 | - | 44.39ms | 4.5G | 1.9M | 3.8MB(FP16) |
| FBNetV5 | 320*640 | 30.4 | - | - | 1.8G | - | - |
| MobileDet | 320*320 | 25.6 | - | - | 0.9G | - | - |
Model checkpoints and weights
Download in the release files.