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Releases: RangiLyu/nanodet

NanoDet-Plus v1.0.0

21 Mar 08:26
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NanoDet-Plus v1.0.0

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.

What's Changed

New Contributors

Full Changelog: v0.4.2...v1.0.0

NanoDet-Plus v1.0.0-alpha

26 Dec 06:28
08bad32
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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.

image

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.

v0.4.2 Fix compatibility

22 Aug 11:20
76e025b
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v0.4.2

Fix some compatibility issue of NanoDet v0.4

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.

Download pretrained models

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

v0.4.1

17 Jul 09:45
be08279
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v0.4.1

This is a final release of NanoDet v0.x.

I'm doing a lot of refactoring. NanoDet v1.x is coming soon.

Download pretrained models

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

v0.4.0

08 Jun 03:03
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What's new in v0.4.0

  1. Fix a little bug in demo.py by BlainWu (#210)
  2. Add script to export TorchScript model by strawberrypie (#211)
  3. Use fixed output names when exporting ONNX (#218)
  4. Use scale_factor instead of fixed size in resize to support dynamic shape inference (#218)
  5. Ensure num_classes equal len(class_names) by ZHEQIUSHUI (#221)
  6. Fix a bug in mnn demo while using GPU device by AcherStyx (#234)
  7. Fix with_last_conv bug in shufflenet (#239)
  8. Support batch eval (#241)
  9. Add nanodet-m-1.5x models (#242)
  10. Update model benchmark (#246)
  11. Prevent lightning Trainer from disabling cudnn.benchmark (#249)
  12. Fix multi-GPU evaluation bug with pytorch-lightning (#254)

Download pretrained models

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

Download ncnn models below

v0.3.0 Release

11 Apr 16:09
927143a
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What's new in v0.3.0

  1. Refactor training and testing code with pytorch-lightning.
  2. Solving ONNX inference AxisError by zshn25 (#198).

Download pretrained models

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

v0.2.0 Release

29 Mar 12:33
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What's new in v0.2.0

  1. Add pyncnn demo by caishanli (#167).
  2. Fix ncnn demo build failure without vulkan by nihui (#168).
  3. Add NanoDet-t with Transformer Attention Network (#183).
  4. Add Notebook demo by zhiqwang (#188).
  5. Add feature of saving demo inference result by wwdok (#191).
  6. Fix utf-8 decode bug (#184).
  7. Fix test bug.

Download pretrained models

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

v0.1.0 Release

07 Mar 04:54
2f7364b
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What's new in v0.1.0

  1. Support MNN python and cpp inference (#83 ).
  2. Support OpenVINO inference.
  3. Support libtorch inference experimentally.
  4. Add NanoDet-g.
  5. Add EfficientNet-Lite and Rep-VGG backbone.
  6. Add Model Zoo and provide more pre-trained model.
  7. Refactor GFL head (#154 ).

Download pretrained models

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

v0.0.1

22 Nov 11:17
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v0.0.1 Pre-release
Pre-release

NanoDet ncnn model released.