Open Images Dataset for Object Detection and Classification

Open Images is a large-scale collection of images with annotations for object detection and classification. The dataset was created by Google and uploaded to Hugging Face by 'dalle-mini' in September 2021. It contains millions of images with bounding box and label annotations.

Use Cases

• Train object detection models using bounding box annotations and image labels.
• Develop image classification systems leveraging the provided class labels for millions of images.
• Create multimodal models by combining image data with associated text annotations or captions.
• Benchmark model performance on a large-scale, publicly available visual dataset.

Strengths

• Contains millions of images, enabling training of large-scale models.
• Provides bounding box and label annotations for object detection tasks.
• Hosted on Hugging Face, facilitating easy access and integration with ML pipelines.

Limitations

• Last updated in 2021, which may not reflect recent visual data or objects.
• Specific row counts, class distributions, and geographic coverage details are unknown from provided metadata.

Provenance

Source

Google

Collection Method

Curated and annotated image collection, originally released by Google Research.

Freshness

Last updated on 2021-09-10.

Geography

Region tag indicates 'us', suggesting a potential US focus, but specific coverage is unknown.