Ian100/ProbeScout-tasks

ProbeScout Main17 task packages

Use with ProbeScout source and setup instructions. Download only the dataset(s) you need, and extract each ZIP into the code repository root. The ZIP paths already include dataset/ and visual_analytics/.

Each package includes task definitions, attributes, query image IDs, ordered records, original VQA source/fit/Validation labels, fixed partition contracts, Web score/rank/projection/cluster arrays and the pinned initial F0 evidence. There are no original images, query pictures, thumbnails, historical human feedback, user sessions or human-refined models. Images are supplied locally by the user from the original dataset distributions under their respective terms.

task_packages.json records ZIP and extracted manifest SHA-256 values. Each extracted dataset/tasks/<dataset>/manifest.json records every payload hash. The source release pins the immutable HF commit and manifest identities. Original Val document hashes inside validationProvenance identify the original training inputs, not the sanitized exported files; the latter have separate hashes. This preserves compatibility with the published immutable probe banks.

After extracting and placing local images as documented:

uv run --project probe_learning python scripts/prepare_web.py --dataset cars
cd visual_analytics/pcp_analyze/web
npm run dev

Use --dataset hico, --dataset celeba, or multiple dataset names as appropriate. --without-images prepares numerical views/training but cannot display photographs. Cached browsing and weight refinement use the task package; training or native probe updates additionally require features and, for updates, pretrained banks. Alternatively extract your own matching SigLIP features using the source scripts. The original Cars, HICO-DET and CelebA dataset terms still apply.