ImageNet
ImageNet is one of the largest and most influential image classification datasets. It contains millions of images carefully classified according to a hierarchy inspired by WordNet. This dataset played a major role in the development of convolutional neural networks (CNN) and was the source of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) competition, which marked a significant advance in Computer Vision.
1,281,167 images (training), 50,000 images (validation) and 100,000 images (test), 1000 object classes
Usable for non-commercial research purposes
Description
ImageNet is one of the world's largest annotated image datasets, designed for large-scale image classification. It contains more than 14 million images grouped into more than 20,000 categories (or “synsets”) derived from the WordNet lexical database.
For more than one million of these images, annotations were manually validated, allowing Computer Vision models to be trained with great precision.
The dataset is best known for being the basis of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) competition, which accelerated advances in Computer Vision, especially with the emergence of deep convolutional neural networks (CNNs), such as AlexNet in 2012.
The most used subset (ILSVRC) contains approximately:
- 1.2 million images for training
- 50,000 for validation
- 100,000 for the tests
Classified into 1,000 object categories.
What is this dataset for?
ImageNet is a reference in the field of Computer Vision and is used for:
- Training large-scale image classification models
- Comparative evaluation of new CNN or Transformers architectures
- Learning transfer, where models pre-trained on ImageNet are used as a basis for other tasks (detection, segmentation, etc.)
- Academic benchmarks: this is a standard for testing the performance of AI models on visual recognition tasks.
Can it be enriched or improved?
Yes, although very comprehensive, ImageNet has some limitations and can be improved:
- Add contextual annotations: some images lack metadata or scene details.
- Improving geographic and cultural diversity: ImageNet has been criticized for a certain Western bias.
- Class refinement: some categories are redundant or ambiguous and can be restructured for more specialized uses.
- Application to specific fields: by combining ImageNet with medical, industrial or environmental images, we can create models that are better suited to professional contexts.
🔎 In summary
🧠 Recommended for
- Students or researchers wishing to learn about Deep learning in computer vision
- AI engineers looking for a transfer base robust for new datasets
- Businesses developing generic or industrial object models, in combination with other specialized datasets
🔧 Compatible tools
- Label Studio (enrichment or correction of annotations)
- TensorFlow/PyTorch (tutorials and loaders available)
- CVAT, VGG Image Annotator (additional export/labeling)
💡 Tip
Many open-source models (ResNet, EfficientNet, ViT...) are pre-trained on ImageNet. Use them to save time and improve your performance from the start.
Frequently Asked Questions
Can ImageNet be used for professional or commercial projects?
Yes, we're leaving. Some portions of ImageNet are available under licenses restricted to non-commercial uses. It is therefore essential to check the license of use specific to each subset of the dataset. For commercial projects, it is recommended to use only images that are consistently listed as freely usable or to move towards open source alternatives with clear user rights.
Why is ImageNet still a reference when other more recent datasets exist?
ImageNet remains essential because it allowed the emergence of the first major high-performance and standardized Computer Vision models. The hierarchical structure of its categories, its size, and the ILSVRC competition have made it a universal training base. Many pre-trained models are still based on ImageNet, making it easy to transfer learning. That said, it is often combined with other specialized data sets for more recent tasks (segmentation, multimodality, etc.).
Does ImageNet contain biases? Can they be corrected?
Yes, ImageNet contains biases, in particular cultural, geographical or related to the representation of certain human or social categories. These biases can impact the performance and fairness of models. Several initiatives have been launched to clean, reorganize, or relabel certain parts of the dataset. For sensitive or inclusive projects, it is strongly recommended to complete ImageNet with more representative datasets or to enrich the annotations via collaborative tools.
