ImageNet21K: Recaptioned Large-Scale Image Dataset with 13 Million Examples

ImageNet21K is a large-scale image dataset containing approximately 13 million examples across about 19 thousand classes. The dataset was recaptioned and uploaded by author gmongaras to the Hugging Face platform, with a last recorded update on 2025-02-03. Images are stored in PNG format and can be decoded from raw bytes.

Use Cases

• Training large-scale image classification models based on the 19 thousand class labels.
• Benchmarking model performance on a diverse set of visual concepts based on the 13 million image examples.
• Fine-tuning pre-trained models for transfer learning based on the recaptioned image data.
• Conducting research on dataset scaling and long-tail recognition based on the large number of classes.

Strengths

• Contains approximately 13 million image examples, providing a large-scale resource.
• Covers about 19 thousand distinct visual classes, offering broad semantic coverage.
• Images are provided in PNG format, a standard and widely supported lossless image format.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset description notes a discrepancy, having about 19K classes instead of the expected 21K.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Based on the ImageNet21K dataset, uploaded by gmongaras on Hugging Face.

Collection Method

The description states the images have been recaptioned, but the original collection method is not specified.

Freshness

Last updated 2025-02-03 04:57:53; freshness should be verified.