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Dataset Card for ImageNet (ILSVRC2012)
Dataset Details
Dataset Description
ImageNet (ILSVRC2012) is a large-scale dataset for visual recognition, consisting of 1,281,167 training images and 50,000 validation images across 1,000 object categories. The dataset was designed to facilitate large-scale image classification and object detection research.
Dataset Sources
- Homepage: https://www.image-net.org/challenges/LSVRC/2012/index.php
- Paper: Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115, 211-252.
Dataset Structure
Total images: 1,331,167
Classes: 1000 categories
Splits:
Train: 1,281,167 images
Validation: 50,000 images
Image specs: Variable resolution, RGB
Example Usage
Below is a quick example of how to load this dataset via the Hugging Face Datasets library.
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("randall-lab/imagenet", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/imagenet", split="validation", trust_remote_code=True)
# Access a sample from the dataset
example = dataset[0]
image = example["image"]
label = example["label"]
image.show() # Display the image
print(f"Label: {label}")
Citation
BibTeX:
@article{russakovsky2015imagenet, title={Imagenet large scale visual recognition challenge}, author={Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and others}, journal={International journal of computer vision}, volume={115}, pages={211--252}, year={2015}, publisher={Springer} }