Update README.md

README.md

@@ -1,3 +1,109 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- image-classification
+language:
+- en
+---
+
+# CIFAR-10 Feature Representations (BTL3)
+
+This dataset contains pre-extracted **feature embeddings** from the CIFAR-10 dataset, produced using several pretrained image classification models.  
+The goal is to enable fast experimentation, classifier prototyping, and model comparison **without needing to train or forward pass large models in Colab**.
+
+---
+
+## Dataset Source
+
+The original CIFAR-10 dataset is MIT-licensed and available here:  
+https://www.cs.toronto.edu/~kriz/cifar.html
+
+This dataset **does not contain the raw images**, only derived representation vectors.
+
+---
+
+## Models Used for Feature Extraction
+
+| Model           | Input Size | Library / Weights                                     | Feature Representation         | Output Dim |
+| --------------- | ---------- | ----------------------------------------------------- | ------------------------------ | ---------- |
+| ResNet-50       | 224×224    | torchvision (`ResNet50_Weights.IMAGENET1K_V1`)        | Global average pooled          | **2048**   |
+| VGG-16          | 224×224    | torchvision (`VGG16_Weights.IMAGENET1K_V1`)           | FC6 layer output               | **4096**   |
+| EfficientNet-B0 | 224×224    | torchvision (`EfficientNet_B0_Weights.IMAGENET1K_V1`) | Global average pooled          | **1280**   |
+| ViT-Base/16     | 224×224    | timm (`vit_base_patch16_224`)                         | CLS token embedding            | **768**    |
+| Swin-Base       | 224×224    | timm (`swin_base_patch4_window7_224`)                 | Global mean pooled final stage | **1024**   |
+
+Each model produces a different feature dimensionality depending on its architecture.
+
+---
+
+## File Format
+
+All feature data is stored in **compressed `.npz`** format:
+
+```
+model_name/
+train_features.npz
+test_features.npz
+````
+
+Each `.npz` file contains:
+- **`features`** → Feature vectors of shape `(N, D)`
+- **`labels`** → Corresponding class labels `(N,)`
+
+Example:  
+If using ResNet-50 → `(50000, 2048)` for training features.
+
+---
+
+## Loading the Features in Python
+
+```python
+from huggingface_hub import hf_hub_download
+import numpy as np
+
+def load_features(model_name, split="train"):
+    file_path = hf_hub_download(
+        repo_id="LeTienDat/BTL3_CIFAR-10",
+        filename=f"{model_name}/{split}_features.npz"
+    )
+    data = np.load(file_path)
+    return data["features"], data["labels"]
+
+# Example usage
+X_train, y_train = load_features("resnet50", "train")
+X_test, y_test = load_features("resnet50", "test")
+
+print(X_train.shape, y_train.shape)
+````
+
+---
+
+## License
+
+This dataset is released under the **CC-BY 4.0** license.
+
+You are free to:
+
+* Use
+* Modify
+* Share
+* Publish results
+
+As long as you **credit this dataset repository**.
+
+Original CIFAR-10 dataset is MIT licensed.
+
+---
+
+## Citation
+
+If you use these features, please cite:
+
+```
+@misc{BTL3_CIFAR10_Features,
+  author = {Le Tien Dat},
+  title = {BTL3 CIFAR-10 Feature Dataset},
+  year = {2025},
+  howpublished = {\url{https://huggingface.co/LeTienDat/BTL3_CIFAR-10}}
+}
+```