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README.md
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## Introduction
<a href="https://github.com/facebookresearch/ConvNeXt">Official Repo</a>
<a href="https://github.com/SegmentationBLWX/sssegmentation/blob/main/ssseg/modules/models/backbones/convnext.py">Code Snippet</a>
<details>
<summary align="left"><a href="https://arxiv.org/pdf/2201.03545.pdf">ConvNeXt (CVPR'2022)</a></summary>
```latex
@article{liu2022convnet,
title={A ConvNet for the 2020s},
author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining},
journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
```
</details>
## Results
#### ADE20k
| Segmentor | Pretrain | Backbone | Crop Size | Schedule | Train/Eval Set | mIoU | Download |
| :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
| UperNet | ImageNet-1k-224x224 | ConvNeXt-T | 512x512 | LR/POLICY/BS/EPOCH: 0.0001/poly/16/130 | train/val | 46.25% | [cfg](https://raw.githubusercontent.com/SegmentationBLWX/sssegmentation/main/ssseg/configs/convnext/upernet_convnexttiny_ade20k.py) | [model](https://github.com/SegmentationBLWX/modelstore/releases/download/ssseg_convnext/upernet_convnexttiny_ade20k.pth) | [log](https://github.com/SegmentationBLWX/modelstore/releases/download/ssseg_convnext/upernet_convnexttiny_ade20k.log) |
| UperNet | ImageNet-1k-224x224 | ConvNeXt-S | 512x512 | LR/POLICY/BS/EPOCH: 0.0001/poly/16/130 | train/val | 48.68% | [cfg](https://raw.githubusercontent.com/SegmentationBLWX/sssegmentation/main/ssseg/configs/convnext/upernet_convnextsmall_ade20k.py) | [model](https://github.com/SegmentationBLWX/modelstore/releases/download/ssseg_convnext/upernet_convnextsmall_ade20k.pth) | [log](https://github.com/SegmentationBLWX/modelstore/releases/download/ssseg_convnext/upernet_convnextsmall_ade20k.log) |
| UperNet | ImageNet-1k-224x224 | ConvNeXt-B | 512x512 | LR/POLICY/BS/EPOCH: 0.0001/poly/16/130 | train/val | 48.97% | [cfg](https://raw.githubusercontent.com/SegmentationBLWX/sssegmentation/main/ssseg/configs/convnext/upernet_convnextbase_ade20k.py) | [model](https://github.com/SegmentationBLWX/modelstore/releases/download/ssseg_convnext/upernet_convnextbase_ade20k.pth) | [log](https://github.com/SegmentationBLWX/modelstore/releases/download/ssseg_convnext/upernet_convnextbase_ade20k.log) |
| UperNet | ImageNet-21k-224x224 | ConvNeXt-B-21k | 640x640 | LR/POLICY/BS/EPOCH: 0.0001/poly/16/130 | train/val | 52.71% | [cfg](https://raw.githubusercontent.com/SegmentationBLWX/sssegmentation/main/ssseg/configs/convnext/upernet_convnextbase21k_ade20k.py) | [model](https://github.com/SegmentationBLWX/modelstore/releases/download/ssseg_convnext/upernet_convnextbase21k_ade20k.pth) | [log](https://github.com/SegmentationBLWX/modelstore/releases/download/ssseg_convnext/upernet_convnextbase21k_ade20k.log) |
| UperNet | ImageNet-21k-224x224 | ConvNeXt-L-21k | 640x640 | LR/POLICY/BS/EPOCH: 0.0001/poly/16/130 | train/val | 53.41% | [cfg](https://raw.githubusercontent.com/SegmentationBLWX/sssegmentation/main/ssseg/configs/convnext/upernet_convnextlarge21k_ade20k.py) | [model](https://github.com/SegmentationBLWX/modelstore/releases/download/ssseg_convnext/upernet_convnextlarge21k_ade20k.pth) | [log](https://github.com/SegmentationBLWX/modelstore/releases/download/ssseg_convnext/upernet_convnextlarge21k_ade20k.log) |
| UperNet | ImageNet-21k-224x224 | ConvNeXt-XL-21k | 640x640 | LR/POLICY/BS/EPOCH: 0.0001/poly/16/130 | train/val | 53.68% | [cfg](https://raw.githubusercontent.com/SegmentationBLWX/sssegmentation/main/ssseg/configs/convnext/upernet_convnextxlarge21k_ade20k.py) | [model](https://github.com/SegmentationBLWX/modelstore/releases/download/ssseg_convnext/upernet_convnextxlarge21k_ade20k.pth) | [log](https://github.com/SegmentationBLWX/modelstore/releases/download/ssseg_convnext/upernet_convnextxlarge21k_ade20k.log) |
## More
You can also download the model weights from following sources:
- BaiduNetdisk: https://pan.baidu.com/s/1gD-NJJWOtaHCtB0qHE79rA with access code **s757**