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} }