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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The Tensorflow, Keras implementation of U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET with optional ImageNet-trained backbones.
| Date | Stars |
|---|---|
| 2026-07-24 | 762 |
| 2026-07-25 | 763 |
| 2026-07-28 | 763 |
| 2026-07-30 | 763 |
| 2026-08-06 | 763 |
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# keras-unet-collection [](https://badge.fury.io/py/keras-unet-collection) [](https://pypi.org/project/keras-unet-collection/) [](https://github.com/yingkaisha/keras-unet-collection/graphs/commit-activity) [](https://zenodo.org/badge/latestdoi/323426984) The `tensorflow.keras` implementation of U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET with optional ImageNet-trained backbones. ---------- `keras_unet_collection.models` contains functions that configure keras models with hyper-parameter options. * Pre-trained ImageNet backbones are supported for U-net, U-net++, UNET 3+, Attention U-net, and TransUNET. * Deep supervision is supported for U-net++, UNET 3+, and U^2-Net. * See the [User guide](https://github.com/yingkaisha/keras-unet-collection/blob/main/examples/user_guide_models.ipynb) for other options and use cases. | `keras_unet_collection.models` | Name | Reference | |:---------------|:----------------|:----------------| | `unet_2d` | U-net | [Ronneberger et al. (2015)](https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28) | | `vnet_2d` | V-net (modified for 2-d inputs) | [Milletari et al. (2016)](https://arxiv.org/abs/1606.04797) | | `unet_plus_2d` | U-net++ | [Zhou et al. (2018)](https://link.springer.com/chapter/10.1007/978-3-030-00889-5_1) | | `r2_unet_2d` | R2U-Net | [Alom et al. (2018)](https://arxiv.org/abs/1802.06955) | | `att_unet_2d` | Attention U-net | [Oktay et al. (2018)](https://arxiv.org/abs/1804.03999) | | `resunet_a_2d` | ResUnet-a | [Diakogiannis et al. (2020)](https://doi.org/10.1016/j.isprsjprs.2020.01.013) | | `u2net_2d` | U^2-Net | [Qin et al. (2020)](https://arxiv.org/abs/2005.09007) | | `unet_3plus_2d` | UNET 3+ | [Huang et al. (2020)](https://arxiv.org/abs/2004.08790) | | `transunet_2d` | TransUNET | [Chen et al. (2021)](https://arxiv.org/abs/2102.04306) | | `swin_unet_2d` | Swin-UNET | [Hu et al. (2021)](https://arxiv.org/abs/2105.05537) | **Note**: the two Transformer models are incompatible with `NumPy 1.20`; `NumPy 1.19.5` is recommended. ---------- ` keras_unet_collection.base` contains functions that build the base architecture (i.e., without model heads) of Unet variants for model customization and debugging. | ` keras_unet_collection.base` | Notes | |:-----------------------------------|:------| | `unet_2d_base`, `vnet_2d_base`, `unet_plus_2d_base`, `unet_3plus_2d_base`, `att_unet_2d_base`, `r2_unet_2d_base`, `resunet_a_2d_base`, `u2net_2d_base`, `transunet_2d_base`, `swin_unet_2d_base` | Functions that accept an input tensor and hyper-parameters of the corresponded model, and produce output tensors of the base architecture. | ---------- `keras_unet_collection.activations` and `keras_unet_collection.losses` provide additional activation layers and loss functions. | `keras_unet_collection.activations` | Name | Reference | |:--------|:----------------|:----------------| | `GELU` | Gaussian Error Linear Units (GELU) | [Hendrycks et al. (2016)](https://arxiv.org/abs/1606.08415) | | `Snake` | Snake activation | [Liu et al. (2020)](https://arxiv.org/abs/2006.08195) | | `keras_unet_collection.losses` | Name | Reference | |:----------------|:----------------|:----------------| | `dice` | Dice loss | [Sudre et al. (2017)](https://link.springer.com/chapter/10.1007/978-3-319-67558-9_28) | | `tversky` | Tversky loss | [Hashemi et al. (2018)](https://ieeexplore.ieee.org/abstract/document/8573779) | | `focal_tversky` | Focal Tversky loss | [Abraham et al. (2019)](https://ieeexplore.ieee.org/abstract/document/
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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