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README.md

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@@ -19,7 +19,7 @@ Segmentation based on [PyTorch](https://pytorch.org/).**
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The main features of this library are:
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- High-level API (just two lines to create a neural network)
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- 10 models architectures for binary and multi class segmentation (including legendary Unet)
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- 11 models architectures for binary and multi class segmentation (including legendary Unet)
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- 124 available encoders (and 500+ encoders from [timm](https://github.com/rwightman/pytorch-image-models))
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- All encoders have pre-trained weights for faster and better convergence
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- Popular metrics and losses for training routines
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- DeepLabV3 [[paper](https://arxiv.org/abs/1706.05587)] [[docs](https://smp.readthedocs.io/en/latest/models.html#deeplabv3)]
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- DeepLabV3+ [[paper](https://arxiv.org/abs/1802.02611)] [[docs](https://smp.readthedocs.io/en/latest/models.html#id9)]
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- UPerNet [[paper](https://arxiv.org/abs/1807.10221)] [[docs](https://smp.readthedocs.io/en/latest/models.html#upernet)]
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- Segformer [[paper](https://arxiv.org/abs/2105.15203)] [[docs](https://smp.readthedocs.io/en/latest/models.html#segformer)]
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#### Encoders <a name="encoders"></a>
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