| """data_label_factory.identify — open-set retrieval / card identification. |
| |
| The companion to the bbox-grounding pipeline. Where the main `data_label_factory` |
| CLI produces COCO labels for training a closed-set detector, this subpackage |
| produces a CLIP-based retrieval index for open-set identification. |
| |
| Use it when you have a known set of N reference images (cards, products, parts, |
| artworks, etc) and want to identify which one a webcam frame is showing — with |
| a single reference image per class and zero training time. |
| |
| Pipeline stages |
| --------------- |
| |
| references/ ← user provides 1 image per class |
| ↓ |
| train_identifier ← optional: contrastive fine-tune of a small |
| ↓ projection head on top of frozen CLIP |
| clip_proj.pt |
| ↓ |
| build_index ← CLIP-encode each reference + apply projection |
| ↓ head, save embeddings to .npz |
| card_index.npz |
| ↓ |
| verify_index ← self-test: each reference should match itself |
| ↓ as top-1 with high cosine similarity |
| serve_identifier ← HTTP server (mac_tensor /api/falcon-shaped) |
| ↓ that the live tracker UI talks to |
| /api/falcon |
| |
| This is the data-label-factory loop applied to retrieval instead of detection. |
| |
| CLI |
| --- |
| |
| python3 -m data_label_factory.identify train --refs limit-over-pack/ --out clip_proj.pt |
| python3 -m data_label_factory.identify index --refs limit-over-pack/ --proj clip_proj.pt --out card_index.npz |
| python3 -m data_label_factory.identify verify --index card_index.npz --refs limit-over-pack/ |
| python3 -m data_label_factory.identify serve --index card_index.npz --refs limit-over-pack/ --port 8500 |
| """ |
|
|
| __all__ = ["main"] |
|
|
|
|
| def main(): |
| """Lazy entry point — only imports the heavy ML deps if user invokes the CLI.""" |
| from .cli import main as _main |
| return _main() |
|
|
