Update README.md

README.md

@@ -11,28 +11,28 @@ dataset_info:
           '1': '1'
   splits:
   - name: test
-    num_bytes: 751052953.0
+    num_bytes: 751052953
     num_examples: 32768
   - name: train
-    num_bytes: 6116336231.0
+    num_bytes: 6116336231
     num_examples: 262144
   - name: val
-    num_bytes: 768776855.0
+    num_bytes: 768776855
     num_examples: 32768
   - name: train800
-    num_bytes: 18620655.0
+    num_bytes: 18620655
     num_examples: 800
   - name: val200
-    num_bytes: 4690607.0
+    num_bytes: 4690607
     num_examples: 200
   - name: train800val200
-    num_bytes: 23311262.0
+    num_bytes: 23311262
     num_examples: 1000
   - name: trainval
-    num_bytes: 6885113086.0
+    num_bytes: 6885113086
     num_examples: 294912
   download_size: 14581924165
-  dataset_size: 14567901649.0
+  dataset_size: 14567901649
 configs:
 - config_name: default
   data_files:
@@ -50,4 +50,20 @@ configs:
     path: data/train800val200-*
   - split: trainval
     path: data/trainval-*
+license: cc0-1.0
 ---
+
+
+# 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](https://bramtoula.github.io/combo/) (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](https://arxiv.org/abs/1910.04867)
+- **VTAB Repository:** [google-research/task_adaptation](https://github.com/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](http://arxiv.org/abs/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](https://doi.org/10.1001/jama.2017.14585).
+- **Original Homepage:** [PatchCamelyon GitHub](https://github.com/basveeling/pcam)