Parasites 1S07H is an object detection dataset containing 2,110 annotated microscopy images across 8 parasite species. The dataset is part of the Roboflow 100 benchmark, curated by LibreYOLO, and was last updated on Hugging Face in January 2026. It is split into 1,484 training, 411 validation, and 215 test images.
Use Cases
- Train object detection models to identify Ancylostoma Spp based on annotated microscopy images.
- Benchmark model performance on the Ascaris Lumbricoides class as part of the Roboflow 100 collection.
- Fine-tune detection algorithms for the Enterobius Vermicularis parasite using the provided image splits.
- Evaluate model generalization across 8 parasite species, including Fasciola Hepatica and Schistosoma.
- Develop automated diagnostic tools for parasitology based on the annotated image dataset.
Strengths
- Contains 2,110 annotated images, providing a substantial base for model training.
- Includes 8 distinct parasite classes, enabling multi-class detection tasks.
- Provides predefined train/validation/test splits (1,484/411/215 images) for standardized evaluation.
- Part of the Roboflow 100 benchmark, suggesting a degree of curation and peer usage.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Freshness should be verified as the last update timestamp is in the future (2026-01-20).
Provenance
- Source
- LibreYOLO via Hugging Face, part of the Roboflow 100 benchmark.
- Collection Method
- Likely collected and annotated from microscopy imagery.
- Time Range
- null
- Freshness
- Last updated 2026-01-20 23:29:43; freshness should be verified.
- Geography
- null