BloodshotNet: Large-Scale Computer Vision Dataset for Blood Detection

BloodshotNet-Dataset is the official, large-scale aggregated dataset designed to train a YOLO-based blood detection model. The dataset was created by author 'petre-bit' and was last updated on Hugging Face in April 2026. It contains highly graphic and sensitive imagery, including simulated and real blood, serious injury, and surgical scenes.

Use Cases

• Training object detection models for blood and injury identification based on the described graphic imagery.
• Developing content moderation systems to filter violent or gory images based on the dataset's focus.
• Benchmarking model robustness on sensitive and challenging visual data as implied by the dataset's purpose.
• Researching computer vision applications in medical or forensic contexts involving blood detection.

Strengths

• Described as 'large-scale' and 'aggregated', suggesting a substantial collection.
• Specifically designed for training a named model (BloodshotNet), indicating a clear purpose.
• Last update timestamp is provided (2026-04-15 23:54:05), showing recent activity.

Limitations

• Description metadata is limited; actual data quality, size, and format require manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset contains highly graphic content, which restricts its use and requires careful ethical consideration.

Provenance

Source

Hugging Face, uploaded by author 'petre-bit'.

Collection Method

Aggregated, likely from multiple sources, as described.

Time Range

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Freshness

Last updated 2026-04-15 23:54:05; freshness should be verified.

Geography

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