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---
title: README
emoji: 🏃
colorFrom: gray
colorTo: red
sdk: static
pinned: false
---
**Github repository:** https://github.com/Franblueee/torchmil
[**torchmil**](https://github.com/Franblueee/torchmil) is a [PyTorch](https://pytorch.org/)-based library for deep Multiple Instance Learning (MIL).
It provides a simple, flexible, and extensible framework for working with MIL models and data.
It includes:
- A collection of popular [MIL models](https://franblueee.github.io/torchmil/api/models/).
- Different [PyTorch modules](https://franblueee.github.io/torchmil/api/nn/) frequently used in MIL models.
- Handy tools to deal with [MIL data](https://franblueee.github.io/torchmil/api/data/).
- A collection of popular [MIL datasets](https://franblueee.github.io/torchmil/api/datasets/).
## Installation
```bash
pip install torchmil
```
## Quick start
You can load a MIL dataset and train a MIL model in just a few lines of code:
```python
from torchmil.datasets import Camelyon16MIL
from torchmil.models import ABMIL
from torchmil.utils import Trainer
from torchmil.data import collate_fn
from torch.utils.data import DataLoader
# Load the Camelyon16 dataset
dataset = Camelyon16MIL(root='data', features='UNI')
dataloader = DataLoader(dataset, batch_size=4, shuffle=True, collate_fn=collate_fn)
# Instantiate the ABMIL model and optimizer
model = ABMIL(in_shape=(2048,), criterion=torch.nn.BCEWithLogitsLoss()) # each model has its own criterion
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
# Instantiate the Trainer
trainer = Trainer(model, optimizer, device='cuda')
# Train the model
trainer.train(dataloader, epochs=10)
# Save the model
torch.save(model.state_dict(), 'model.pth')
```
## Next steps
You can take a look at the [examples](https://franblueee.github.io/torchmil/examples/) to see how to use **torchmil** in practice.
To see the full list of available models, datasets, and modules, check the [API reference](https://franblueee.github.io/torchmil/api/).
## Contributing to torchmil
We welcome contributions to **torchmil**! There several ways you can contribute:
- Reporting bugs or issues you encounter while using the library, asking questions, or requesting new features: use the [Github issues](https://github.com/Franblueee/torchmil/issues).
- Improving the documentation: if you find any part of the documentation unclear or incomplete, feel free to submit a pull request with improvements.
- If you have a new model, dataset, or utility that you think would be useful for the community, please consider submitting a pull request to add it to the library.
Take a look at [CONTRIBUTING.md](https://github.com/Franblueee/torchmil/blob/main/CONTRIBUTING.md) for more details on how to contribute.
## Citation
If you find this library useful, please consider citing it:
```bibtex
@article{castro2025torchmil,
title={torchmil: A PyTorch-based library for deep Multiple Instance Learning},
author={Castro-Mac{\'\i}as, Francisco M and S{\'a}ez-Maldonado, Francisco J and Morales-{\'A}lvarez, Pablo and Molina, Rafael},
journal={arXiv preprint arXiv:2509.08129},
year={2025}
}
``` |