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traiNNer: Deep learning framework for image and video super-resolution, restoration and image-to-image translation, for training and testing.
| Date | Stars |
|---|---|
| 2026-07-24 | 308 |
| 2026-07-25 | 308 |
| 2026-07-28 | 308 |
| 2026-07-30 | 308 |
| 2026-07-31 | 308 |
| 2026-08-06 | 308 |
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# traiNNer
[](https://python.org)
[](https://github.com/victorca25/traiNNer/blob/master/LICENSE)
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traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation toolbox based on PyTorch.
Here you will find: boilerplate code for training and testing computer vision (CV) models, different methods and strategies integrated in a single pipeline and modularity to add and remove components as needed, including new network architectures and templates for different training strategies. The code is under a constant state of change, so if you find an issue or bug please open a [issue](https://github.com/victorca25/traiNNer/issues), a [discussion](https://github.com/victorca25/traiNNer/discussions) or write in one of the [Discord channels](##additional-help) for help.
Different from other repositories, here the focus is not only on repeating previous papers' results, but to enable more people to train their own models more easily, using their own custom datasets, as well as integrating new ideas to increase the performance of the models. For these reasons, a lot of the code is made in order to automatically take care of fixing potential issues, whenever possible.
Details of the currently supported architectures can be found [here](https://github.com/victorca25/traiNNer/blob/master/docs/architectures.md).
For a changelog and general list of features of this repository, check [here](https://github.com/victorca25/traiNNer/blob/master/docs/changes.md).
## Table of Contents
1. [Dependencies](#dependencies)
2. [Codes](#codes)
3. [Usage](#usage)
4. [Pretrained models](#pretrained-models)
5. [Datasets](#datasets)
6. [How to help](#how-to-help)
### Dependencies
- Python 3 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux))
- [PyTorch >= 0.4.0](https://pytorch.org/). PyTorch >= 1.7.0 required to enable certain features (SWA, AMP, others), as well as [torchvision](https://pytorch.org/vision/stable/index.html).
- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
- Python packages: `pip install numpy opencv-python`
- `JSON` files can be used for the configuration option files, but in order to use `YAML`, the `PyYAML` python package is also a dependency: [`pip install PyYAML`](https://pyyaml.org/)
#### Optional Dependencies
- Python package: [`pip install tensorboardX`](https://github.com/lanpa/tensorboardX), for visualizing curves.
- Python package: [`pip install lmdb`](https://github.com/jnwatson/py-lmdb), for lmdb database support.
- Python package: [`pip install scipy`](https://www.scipy.org/) to use [CEM](https://github.com/victorca25/traiNNer/blob/master/codes/models/modules/architectures/CEM/README.md).
- Python package: [`pip install Pillow`](https://python-pillow.org/) to use as an alternative image backend (default is OpenCV).
- Python package: [`pip install joblib`](https://joblib.readthedocs.io/) to train White-box Cartoonization (WBC) models.
## Codes
This repository is a full framework for training different kinds of networks, with multiple enhancements and options. In [`./codes`](https://github.com/victorca25/traiNNer/tree/master/codes) you will find a more detailed explaination of the **code framework** ).
You will also find:
1. Some useful scripts. More details in [`./codes/scripts`](https://github.com/victorca25/tExcerpt of 12,911 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ec293784d856bb47, topic:super-resolution, desc:super resolution, readme:super resolution