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NanoDet-Plus v1.0.0.zip (1.7 MB)
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| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| Parent folder | |||
| ncnn-nanodet-m.zip | 2021-06-08 | 1.7 MB | |
| ncnn-nanodet-m-int8.zip | 2021-06-08 | 910.1 kB | |
| ncnn-nanodet-m-416.zip | 2021-06-08 | 1.7 MB | |
| ncnn-nanodet-m-416-int8.zip | 2021-06-08 | 903.8 kB | |
| ncnn-nanodet-m-1.5x.zip | 2021-06-08 | 3.8 MB | |
| ncnn-nanodet-m-1.5x-int8.zip | 2021-06-08 | 1.9 MB | |
| ncnn-nanodet-m-1.5x-416.zip | 2021-06-08 | 3.8 MB | |
| ncnn-nanodet-m-1.5x-416-int8.zip | 2021-06-08 | 1.9 MB | |
| README.md | 2021-06-08 | 2.8 kB | |
| v0.4.0.tar.gz | 2021-06-08 | 1.3 MB | |
| v0.4.0.zip | 2021-06-08 | 1.3 MB | |
| Totals: 11 Items | 19.4 MB | 1 | |
What's new in v0.4.0
- Fix a little bug in demo.py by BlainWu (#210)
- Add script to export TorchScript model by strawberrypie (#211)
- Use fixed output names when exporting ONNX (#218)
- Use scale_factor instead of fixed size in resize to support dynamic shape inference (#218)
- Ensure num_classes equal len(class_names) by ZHEQIUSHUI (#221)
- Fix a bug in mnn demo while using GPU device by AcherStyx (#234)
- Fix with_last_conv bug in shufflenet (#239)
- Support batch eval (#241)
- Add nanodet-m-1.5x models (#242)
- Update model benchmark (#246)
- Prevent lightning Trainer from disabling cudnn.benchmark (#249)
- 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 |