Add comprehensive dataset card with FL usage instructions

README.md

@@ -1,44 +1,120 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': actinic keratoses and intraepithelial carcinoma
-          '1': basal cell carcinoma
-          '2': benign keratosis-like lesions
-          '3': dermatofibroma
-          '4': melanoma
-          '5': melanocytic nevi
-          '6': vascular lesions
-  - name: label_name
-    dtype:
-      class_label:
-        names:
-          '0': actinic keratoses and intraepithelial carcinoma
-          '1': basal cell carcinoma
-          '2': benign keratosis-like lesions
-          '3': dermatofibroma
-          '4': melanoma
-          '5': melanocytic nevi
-          '6': vascular lesions
-  splits:
-  - name: train
-    num_bytes: 9423339.125
-    num_examples: 7007
-  - name: test
-    num_bytes: 2695597.375
-    num_examples: 2005
-  download_size: 12505595
-  dataset_size: 12118936.5
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
----
+    license: cc-by-nc-4.0
+    task_categories:
+    - image-classification
+    tags:
+    - medmnist
+    - medical-imaging
+    - skin-cancer
+    - federated-learning
+    - healthcare
+    size_categories:
+    - 10K<n<100K
+    ---
+
+    # DermaMNIST Dataset for Federated Learning
+
+    ## Dataset Description
+
+    **DermaMNIST** is a multi-class dataset of dermatoscopic images for skin lesion classification. It is part of the [MedMNIST v2](https://medmnist.com/) collection, a set of standardized biomedical image datasets for machine learning. This version of the dataset has been prepared for easy use in federated learning simulations, particularly with the [Flower](https://flower.dev/) framework.
+
+    The dataset is based on the [HAM10000](https://doi.org/10.1038/sdata.2018.161) dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. The images are 28x28 pixels and resized from their original 600x450 resolution.
+
+    ### Classes
+    The dataset contains 7 classes of skin lesions:
+    - **0**: `actinic keratoses and intraepithelial carcinoma`
+- **1**: `basal cell carcinoma`
+- **2**: `benign keratosis-like lesions`
+- **3**: `dermatofibroma`
+- **4**: `melanoma`
+- **5**: `melanocytic nevi`
+- **6**: `vascular lesions`
+
+    ### Dataset Statistics
+    - **Number of classes**: 7
+    - **Training samples**: 7,007
+    - **Test samples**: 2,005
+    - **Total samples**: 9,012
+
+    ## Dataset Structure
+
+    ### Data Fields
+    - `image`: A PIL Image object of size 28x28.
+    - `label`: The integer class ID.
+    - `label_name`: The string name of the class label.
+
+    ### Data Splits
+    - **Train**: 7,007 samples
+    - **Test**: 2,005 samples
+
+    ## Usage with Flower Datasets 🌸
+
+    This dataset is ideal for federated learning simulations where data is distributed across multiple clients (e.g., hospitals). Here's how to use it with [Flower Datasets](https://flower.dev/docs/datasets/):
+
+    ```python
+    from flwr_datasets import FederatedDataset
+
+    # Define the number of clients (e.g., simulating 10 hospitals)
+    NUM_CLIENTS = 10
+
+    # Load the dataset and partition it into 10 clients
+    fds = FederatedDataset(dataset="your-username/dermamnist-fl", partitioners={"train": NUM_CLIENTS})
+
+    # Get the data for a specific client
+    client_data = fds.load_partition(0, "train")
+
+    # You can now use this client_data with your standard PyTorch or TensorFlow data loaders.
+    # For example, to set the format for PyTorch:
+    # client_data.set_format(type="torch", columns=["image", "label"])
+    ```
+    ## Citation
+    If you use this dataset in your research, please cite the following paper:
+
+    ```
+    @article{medmnistv2,
+        title={MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification},
+        author={Jiancheng Yang and Rui Shi and Donglai Wei and Zequan Liu and Lin Zhao and Bilian Ke and Hanspeter Pfister and Bingbing Ni},
+        journal={Scientific Data},
+        year={2023},
+        volume={10},
+        number={1},
+        pages={41},
+        doi={10.1038/s41597-022-01721-8}
+    }
+    ```
+    ## Citation
+    ```bibtex
+        @article{medmnistv2,
+            title={MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification},
+            author={Jiancheng Yang and Rui Shi and Donglai Wei and Zequan Liu and Lin Zhao and Bilian Ke and Hanspeter Pfister and Bingbing Ni},
+            journal={Scientific Data},
+            year={2023},
+            volume={10},
+            number={1},
+            pages={41},
+            doi={10.1038/s41597-022-01721-8}
+        }
+    ```
+    ## Example Usage with datasets
+    ```python
+    from datasets import load_dataset
+    from PIL import Image
+
+    # Load the dataset
+    dataset = load_dataset("your-username/dermamnist-fl")
+
+    # Access the training split
+    train_data = dataset["train"]
+    print(f"Number of training samples: {len(train_data)}")
+
+    # Access a sample
+    sample = train_data[100]
+    image: Image = sample['image']
+    print(f"Image size: {image.size}")
+    print(f"Label: {sample['label']}")
+    print(f"Label name: {sample['label_name']}")
+
+    # To display the image
+    # image.show()
+    ```
+