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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.08129 |
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| _version_ | 1866912579748626432 |
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| author | Castro-Macías, Francisco M. Sáez-Maldonado, Francisco J. Morales-Álvarez, Pablo Molina, Rafael |
| author_facet | Castro-Macías, Francisco M. Sáez-Maldonado, Francisco J. Morales-Álvarez, Pablo Molina, Rafael |
| contents | Multiple Instance Learning (MIL) is a powerful framework for weakly supervised learning, particularly useful when fine-grained annotations are unavailable. Despite growing interest in deep MIL methods, the field lacks standardized tools for model development, evaluation, and comparison, which hinders reproducibility and accessibility. To address this, we present torchmil, an open-source Python library built on PyTorch. torchmil offers a unified, modular, and extensible framework, featuring basic building blocks for MIL models, a standardized data format, and a curated collection of benchmark datasets and models. The library includes comprehensive documentation and tutorials to support both practitioners and researchers. torchmil aims to accelerate progress in MIL and lower the entry barrier for new users. Available at https://torchmil.readthedocs.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08129 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | torchmil: A PyTorch-based library for deep Multiple Instance Learning Castro-Macías, Francisco M. Sáez-Maldonado, Francisco J. Morales-Álvarez, Pablo Molina, Rafael Machine Learning Multiple Instance Learning (MIL) is a powerful framework for weakly supervised learning, particularly useful when fine-grained annotations are unavailable. Despite growing interest in deep MIL methods, the field lacks standardized tools for model development, evaluation, and comparison, which hinders reproducibility and accessibility. To address this, we present torchmil, an open-source Python library built on PyTorch. torchmil offers a unified, modular, and extensible framework, featuring basic building blocks for MIL models, a standardized data format, and a curated collection of benchmark datasets and models. The library includes comprehensive documentation and tutorials to support both practitioners and researchers. torchmil aims to accelerate progress in MIL and lower the entry barrier for new users. Available at https://torchmil.readthedocs.io. |
| title | torchmil: A PyTorch-based library for deep Multiple Instance Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.08129 |