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README.md

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+**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
+@misc{torchmil,
+  author = {Castro-Mac{\'\i}as, Francisco M and S{\'a}ez-Maldonado, Francisco Javier and Morales Alvarez, Pablo and Molina, Rafael},
+  title = {torchmil: A PyTorch-based library for deep Multiple Instance Learning},
+  year = {2025},
+  howpublished = {\url{https://franblueee.github.io/torchmil/}}
+}
+```