10,343 real-world images are organized into 139 semantic categories, including animals, vehicles, and everyday scenes. The dataset was created by darshvit20 for experiments in semantic image retrieval and multimodal embedding search. It was last updated on April 10, —.
Use Cases
- Benchmarking semantic image retrieval models based on the 139 provided categories.
- Training or fine-tuning multimodal embedding models like CLIP on the diverse real-world images.
- Evaluating vector search libraries such as FAISS for image similarity tasks.
- Conducting experiments in zero-shot or few-shot image classification using the semantic categories.
Strengths
- Contains 10,343 real-world images, providing a substantial collection for experiments.
- Images are organized into 139 distinct semantic categories, offering structured diversity.
- Explicitly created for modern tasks like multimodal embedding search with CLIP and FAISS.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- The description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- huggingface user darshvit20
- Collection Method
- Images were automatically collected using the Python icrawler tool.
- Time Range
- null
- Freshness
- Last updated 2026-04-10 09:32:12; freshness should be verified.
- Geography
- null