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LeTienDat/BTL3_CIFAR-10

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… See the full description on the dataset page: https://huggingface.co/datasets/LeTienDat/BTL3_CIFAR-10.

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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}}
}