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