Datasetpublic

egolqa/object_detection

Object detection This directory contains concepts and object detections generated from the original keyframe set. The primary artifacts are concepts.jsonl and detections.jsonl; artifact_metadata.jsonl records the input, model, prompt version, subset, and Git lineage. concepts.jsonl Each line is one ConceptRecord, a short list of general concepts proposed for one keyframe. Common fields: concept_id: stable concept identifier video_id: source video identifier… See the full description on the dataset page: https://huggingface.co/datasets/egolqa/object_detection.

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Object detection

This directory contains concepts and object detections generated from the original keyframe set. The primary artifacts are concepts.jsonl and detections.jsonl; artifact_metadata.jsonl records the input, model, prompt version, subset, and Git lineage.

`concepts.jsonl`

Each line is one ConceptRecord, a short list of general concepts proposed for one keyframe.

Common fields:

—concept_id: stable concept identifier
—video_id: source video identifier
—time_span: [timestamp_ms, timestamp_ms] in source video time
—keyframe_id: referenced canonical keyframe
—concepts: unique general visual concepts used as detection prompts

`detections.jsonl`

Each line is one TrackingDetectionRecord, a detected object in one keyframe.

Common fields:

—detection_id: stable observation identifier
—video_id: source video identifier
—time_span: [timestamp_ms, timestamp_ms] in source video time
—label: detected object class
—bbox: normalized object region with x_min, y_min, x_max, and y_max
—confidence: SAM3 detection score
—concept_id: concept that prompted this detection
—track_id: sparse association within the source shot
—frame_index: source frame index

The published files are merged across all 700 videos. Each JSONL line keeps its video_id, so consumers can filter one video without restoring the former shard directories.

Generation flow

A vision endpoint proposes general concepts from the keyframes. SAM3 verifies and localizes those concepts, then associates matching boxes across adjacent keyframes in the same shot. It publishes concepts and detections separately so visible text remains the responsibility of OCR.

Run detection for the original keyframe set:

bash

make data-detection \
  JOB='concepts detection' \
  INPUT=/data/aic/shared/artifacts/keyframes/keyframes.jsonl \
  OUTPUT=/data/aic/shared/artifacts/object_detection \
  SUBSET=700vid