Metadata-Version: 2.1
Name: torchcross
Version: 0.0.2
Summary: Easy-to-use PyTorch library for cross-domain learning, few-shot learning and meta-learning
Project-URL: Homepage, https://github.com/StefanoWoerner/torchcross
Project-URL: Issue Tracker, https://github.com/StefanoWoerner/torchcross/issues
Author-email: Stefano Woerner <stefano@woerner.eu>
License-File: LICENSE
License-File: LICENSE.LESSER
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Requires-Dist: h5py~=3.8.0
Requires-Dist: numpy~=1.24.3
Requires-Dist: pillow~=9.5.0
Requires-Dist: pyyaml~=6.0
Requires-Dist: torchvision~=0.15.1
Requires-Dist: torch~=2.0.0
Description-Content-Type: text/markdown

# TorchCross
**Easy-to-use PyTorch library for cross-domain learning, few-shot learning and
meta-learning.**

## What is TorchCross?

TorchCross is a PyTorch library for cross-domain learning, few-shot learning and
meta-learning. It provides convenient utilities for creating cross-domain learning
or few-shot learning experiments.

### Package Overview
- `torchcross`: The main package, containing the core functionality of the library.
- `torchcross.data`: Contains the `CrossDomainDataset` and `FewShotDataset`
  classes, which wrap `TaskSource` instances to produce batches for cross-domain
  learning or tasks for few-shot learning experiments.
- `torchcross.data.task`: Contains the `Task` and `TaskDescription` classes, which 
  represent a task in a few-shot learning scenario and a task's metadata, respectively.
- `torchcross.cd` contains functions to create heads, losses and metrics
  for cross-domain learning experiments.

**This library is still in beta. The API is potentially subject to change. Any feedback
is welcome.**

## Installation

The library can be installed via pip:

```bash
pip install torchcross
```


## Examples

See the [`examples`](examples) directory.

