NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
APACHE-2.0 License
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A stable version of NanoDet-Plus with PyTorch 1.x.
It requires pytorch-lightning>=1.9.0,<2.0.0 and torch>=1.10,<2.0.
Full Changelog: https://github.com/RangiLyu/nanodet/compare/v0.4.2...v1.0.0
Published by RangiLyu almost 3 years ago
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 | mAPval0.5:0.95 | 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 | - | - |
Download in the release files.
Published by RangiLyu about 3 years ago
Fix pytorch-lightning compatibility. (#304 #309 )
Fix pytorch1.9 compatibility. (#308 )
Support not raising an error when evaluate with empty results. (#310)
I'm doing a lot of refactoring. NanoDet v1.x is coming soon.
Model | Backbone | Resolution | COCO mAP | FLOPS | Params | Pre-train weight | ncnn model | ncnn-int8 |
---|---|---|---|---|---|---|---|---|
NanoDet-m | ShuffleNetV2 1.0x | 320*320 | 20.6 | 0.72B | 0.95M | Download | Download | Download |
NanoDet-m-416 | ShuffleNetV2 1.0x | 416*416 | 23.5 | 1.2B | 0.95M | Download | Download | Download |
NanoDet-m-1.5x | ShuffleNetV2 1.5x | 320*320 | 23.5 | 1.44B | 2.08M | Download | Download | Download |
NanoDet-m-1.5x-416 | ShuffleNetV2 1.5x | 416*416 | 26.8 | 2.42B | 2.08M | Download | Download | Download |
NanoDet-t | ShuffleNetV2 1.0x | 320*320 | 21.7 | 0.96B | 1.36M | Download | ||
NanoDet-g | Custom CSP Net | 416*416 | 22.9 | 4.2B | 3.81M | Download | ||
NanoDet-EfficientLite | EfficientNet-Lite0 | 320*320 | 24.7 | 1.72B | 3.11M | Download | ||
NanoDet-EfficientLite | EfficientNet-Lite1 | 416*416 | 30.3 | 4.06B | 4.01M | Download | ||
NanoDet-EfficientLite | EfficientNet-Lite2 | 512*512 | 32.6 | 7.12B | 4.71M | Download | ||
NanoDet-RepVGG | RepVGG-A0 | 416*416 | 27.8 | 11.3B | 6.75M | Download |
Published by RangiLyu over 3 years ago
This is a final release of NanoDet v0.x.
I'm doing a lot of refactoring. NanoDet v1.x is coming soon.
Model | Backbone | Resolution | COCO mAP | FLOPS | Params | Pre-train weight | ncnn model | ncnn-int8 |
---|---|---|---|---|---|---|---|---|
NanoDet-m | ShuffleNetV2 1.0x | 320*320 | 20.6 | 0.72B | 0.95M | Download | Download | Download |
NanoDet-m-416 | ShuffleNetV2 1.0x | 416*416 | 23.5 | 1.2B | 0.95M | Download | Download | Download |
NanoDet-m-1.5x | ShuffleNetV2 1.5x | 320*320 | 23.5 | 1.44B | 2.08M | Download | Download | Download |
NanoDet-m-1.5x-416 | ShuffleNetV2 1.5x | 416*416 | 26.8 | 2.42B | 2.08M | Download | Download | Download |
NanoDet-t | ShuffleNetV2 1.0x | 320*320 | 21.7 | 0.96B | 1.36M | Download | ||
NanoDet-g | Custom CSP Net | 416*416 | 22.9 | 4.2B | 3.81M | Download | ||
NanoDet-EfficientLite | EfficientNet-Lite0 | 320*320 | 24.7 | 1.72B | 3.11M | Download | ||
NanoDet-EfficientLite | EfficientNet-Lite1 | 416*416 | 30.3 | 4.06B | 4.01M | Download | ||
NanoDet-EfficientLite | EfficientNet-Lite2 | 512*512 | 32.6 | 7.12B | 4.71M | Download | ||
NanoDet-RepVGG | RepVGG-A0 | 416*416 | 27.8 | 11.3B | 6.75M | Download |
Model | Backbone | Resolution | COCO mAP | FLOPS | Params | Pre-train weight |
---|---|---|---|---|---|---|
NanoDet-m | ShuffleNetV2 1.0x | 320*320 | 20.6 | 0.72B | 0.95M | Download |
NanoDet-m-416 | ShuffleNetV2 1.0x | 416*416 | 23.5 | 1.2B | 0.95M | Download |
NanoDet-m-1.5x | ShuffleNetV2 1.5x | 320*320 | 23.5 | 1.44B | 2.08M | Download |
NanoDet-m-1.5x-416 | ShuffleNetV2 1.5x | 416*416 | 26.8 | 2.42B | 2.08M | Download |
NanoDet-t | ShuffleNetV2 1.0x | 320*320 | 21.7 | 0.96B | 1.36M | Download |
NanoDet-g | Custom CSP Net | 416*416 | 22.9 | 4.2B | 3.81M | Download |
NanoDet-EfficientLite | EfficientNet-Lite0 | 320*320 | 24.7 | 1.72B | 3.11M | Download |
NanoDet-EfficientLite | EfficientNet-Lite1 | 416*416 | 30.3 | 4.06B | 4.01M | Download |
NanoDet-EfficientLite | EfficientNet-Lite2 | 512*512 | 32.6 | 7.12B | 4.71M | Download |
NanoDet-RepVGG | RepVGG-A0 | 416*416 | 27.8 | 11.3B | 6.75M | Download |
Published by RangiLyu over 3 years ago
Model | Backbone | Resolution | COCO mAP | FLOPS | Params | Pre-train weight |
---|---|---|---|---|---|---|
NanoDet-m | ShuffleNetV2 1.0x | 320*320 | 20.6 | 0.72B | 0.95M | Download |
NanoDet-m-416 | ShuffleNetV2 1.0x | 416*416 | 23.5 | 1.2B | 0.95M | Download |
NanoDet-t (NEW) | ShuffleNetV2 1.0x | 320*320 | 21.7 | 0.96B | 1.36M | Download |
NanoDet-g | Custom CSP Net | 416*416 | 22.9 | 4.2B | 3.81M | Download |
NanoDet-EfficientLite | EfficientNet-Lite0 | 320*320 | 24.7 | 1.72B | 3.11M | Download |
NanoDet-EfficientLite | EfficientNet-Lite1 | 416*416 | 30.3 | 4.06B | 4.01M | Download |
NanoDet-EfficientLite | EfficientNet-Lite2 | 512*512 | 32.6 | 7.12B | 4.71M | Download |
NanoDet-RepVGG | RepVGG-A0 | 416*416 | 27.8 | 11.3B | 6.75M | Download |
Published by RangiLyu over 3 years ago
Model | Backbone | Resolution | COCO mAP | FLOPS | Params | Pre-train weight |
---|---|---|---|---|---|---|
NanoDet-m | ShuffleNetV2 1.0x | 320*320 | 20.6 | 0.72B | 0.95M | Download |
NanoDet-m-416 | ShuffleNetV2 1.0x | 416*416 | 23.5 | 1.2B | 0.95M | Download |
NanoDet-t (NEW) | ShuffleNetV2 1.0x | 320*320 | 21.7 | 0.96B | 1.36M | Download |
NanoDet-g | Custom CSP Net | 416*416 | 22.9 | 4.2B | 3.81M | Download |
NanoDet-EfficientLite | EfficientNet-Lite0 | 320*320 | 24.7 | 1.72B | 3.11M | Download |
NanoDet-EfficientLite | EfficientNet-Lite1 | 416*416 | 30.3 | 4.06B | 4.01M | Download |
NanoDet-EfficientLite | EfficientNet-Lite2 | 512*512 | 32.6 | 7.12B | 4.71M | Download |
NanoDet-RepVGG | RepVGG-A0 | 416*416 | 27.8 | 11.3B | 6.75M | Download |
Published by RangiLyu over 3 years ago
Model | Backbone | Resolution | COCO mAP | FLOPS | Params | Pre-train weight |
---|---|---|---|---|---|---|
NanoDet-m | ShuffleNetV2 1.0x | 320*320 | 20.6 | 0.72B | 0.95M | Download |
NanoDet-m-416 | ShuffleNetV2 1.0x | 416*416 | 23.5 | 1.2B | 0.95M | Download |
NanoDet-g | Custom CSP Net | 416*416 | 22.9 | 4.2B | 3.81M | Download |
NanoDet-EfficientLite | EfficientNet-Lite0 | 320*320 | 24.7 | 1.72B | 3.11M | Download |
NanoDet-EfficientLite | EfficientNet-Lite1 | 416*416 | 30.3 | 4.06B | 4.01M | Download |
NanoDet-EfficientLite | EfficientNet-Lite2 | 512*512 | 32.6 | 7.12B | 4.71M | Download |
NanoDet-RepVGG | RepVGG-A0 | 416*416 | 27.8 | 11.3B | 6.75M | Download |