bramtoula/vtab_patch_camelyon
VTAB PatchCamelyon This dataset has been used for the paper Fantastic Features and Where to Find Them: A Probing Method to combine Features from Multiple Foundation Models (NeurIPS 2025). It reproduces the settings (splits, labels) used for the Visual Task Adaptation Benchmark (VTAB). VTAB Paper: A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark VTAB Repository: google-research/task_adaptation Details of the original dataset: Original… See the full description on the dataset page: https://huggingface.co/datasets/bramtoula/vtab_patch_camelyon.
VTAB PatchCamelyon
This dataset has been used for the paper Fantastic Features and Where to Find Them: A Probing Method to combine Features from Multiple Foundation Models (NeurIPS 2025).
It reproduces the settings (splits, labels) used for the Visual Task Adaptation Benchmark (VTAB).
- VTAB Paper: A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
- VTAB Repository: google-research/task_adaptation
Details of the original dataset:
- Original Citations:
- PatchCamelyon: B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling. "Rotation Equivariant CNNs for Digital Pathology". arXiv:1806.03962.
- Original Camelyon16 dataset : Ehteshami Bejnordi et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA: The Journal of the American Medical Association, 318(22), 2199–2210. doi:jama.2017.14585.
- Original Homepage: PatchCamelyon GitHub