# gcr/torch-residual-networks

<https://repo.masters-masters.com/en/repos/gcr/torch-residual-networks>

[Home](https://repo.masters-masters.com/en) › [gcr](https://repo.masters-masters.com/en/owners/gcr) › [torch-residual-networks](https://repo.masters-masters.com/en/repos/gcr/torch-residual-networks)

gcr/torch-residual-networks has 582 stars on GitHub. Its RepoMasters card: Alpha tier. This is a Torch implementation of \["Deep Residual Learning for Image…

| Field | Value |
| --- | --- |
| Owner | [gcr](https://repo.masters-masters.com/en/owners/gcr) |
| Stars | 582 |
| Forks | 145 |
| Language | [Jupyter Notebook](https://repo.masters-masters.com/en/languages/jupyter-notebook) |
| License | Zlib |
| Created | 2015 |
| Tier | [Alpha (250 to 699 ★)](https://repo.masters-masters.com/en/ranks/b_movie) |
| View on GitHub | [gcr/torch-residual-networks](https://github.com/gcr/torch-residual-networks) |

## Repository

This is a Torch implementation of \["Deep Residual Learning for Image Recognition",Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\](http://arxiv.org/abs/1512.03385) the winners of the 2015 ILSVRC and COCO challenges.

The tier for repos with 250 to 699 ★. Published odds per card pulled: 20%. Rarity: Uncommon.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.