{
 "schema_version": 1,
 "generated": "2026-10-24",
 "id": "fengzhizi715-yolowebagentstarter--turn-video-footage-into-labeled-frames",
 "repo": "fengzhizi715/YoloWebAgentStarter",
 "repo_url": "https://github.com/fengzhizi715/YoloWebAgentStarter",
 "repo_slug": "fengzhizi715-yolowebagentstarter",
 "title": "Turn MP4, MOV, or AVI footage into a dataset of frames",
 "page_title": "How to turn MP4, MOV, or AVI footage into a dataset of frames with Yolo Web Agent Starter",
 "summary": "Extract frames from a video in the Import Center and keep each frame traceable to its source.",
 "who": "A field operator or researcher with recorded video who needs training frames without extracting them by hand.",
 "problem": "Pulling usable frames from video by hand is slow and loses track of which clip each frame came from.",
 "outcome": "A new dataset of JPEG frames, each saved with its source video, checksum, task ID, frame number, and timestamp, and split by the chosen strategy.",
 "steps": [
  "Start the workbench with `./run-all.sh`.",
  "Open Dataset, then Import Dataset, and choose the video file option.",
  "Upload an MP4, MOV, or AVI file, name the new dataset, pick the task type and initial split, and set the FPS, time interval, or frame interval.",
  "Review the decoded sample frame, resolution, duration, expected frame count, and estimated JPEG output size, then start the import.",
  "If you choose time-block assignment, set the window and the train/val/test ratios so each block stays in one split, then check the frame split before labeling."
 ],
 "agent_prompt": "In the YoloWebAgentStarter repo at <repo path>, import the video <video file> as a new dataset with a frame interval. Verify that the import finishes and that the frame records keep the source video, checksum, and timestamp. Return the dataset ID and the frame count.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "Consecutive video frames are usually very similar, so random splitting can inflate evaluation results, and the README advises checking whether the time-block strategy fits your capture scenario.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "requirements": [
  "macOS or Linux",
  "Python 3.11 or 3.12",
  "Node.js 20 or newer",
  "A set of images to label, or the built-in tiny demo generator"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/turn-video-footage-into-labeled-frames/",
 "md": "https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/turn-video-footage-into-labeled-frames/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/fengzhizi715-yolowebagentstarter--turn-video-footage-into-labeled-frames.json",
 "api_version": "v1"
}