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name: feature-extraction-verification description: "Extract face.evoLVe embeddings from trained PyTorch checkpoints and evaluate LFW-style verification metrics." disable-model-invocation: true metadata: disco-role: operating license: MIT

feature-extraction-verification

Use this sub-skill when you need to:

• extract embeddings from a trained face.evoLVe PyTorch checkpoint,
• run the torchvision/ImageFolder batch path or the OpenCV single-image path,
• compare v1 and v2 preprocessing, with or without horizontal-flip TTA,
• compute ROC, threshold accuracy, or VAL-style verification metrics from embeddings,
• debug checkpoint, backbone, shape, or pair-layout mismatches.

Do not use this sub-skill to train a model, create checkpoints, download data, or align raw faces first. Route those requests to the other face-evolve sub-skills instead.

Read / run

• references/feature-and-verification-workflows.md — read this first for the end-to-end extraction and verification flow, preprocessing choices, TTA, and perform_val semantics.
• references/api-reference.md — read this when you need exact function signatures, tensor or array shapes, or metric return values.
• references/troubleshooting.md — read this when loading checkpoints, matching backbones, validating pair counts, or locating bcolz-style validation data.
• scripts/extract_features.py — run this to produce .npy embeddings from a trained checkpoint using either an ImageFolder root or a single image file.
• scripts/evaluate_pairs.py — run this to score embeddings against an issame array and export ROC / accuracy / VAL metrics as JSON.

Routing boundaries

• Checkpoint creation, backbone training, head/loss selection, and optimizer setup belong to pytorch-training.
• Image acquisition, dataset layout, and identity-folder preparation belong to data-preparation.
• Alignment or MTCNN crop before feature extraction belongs to face-alignment.
• PaddlePaddle extraction or verification requests belong to paddle-workflows.

What this sub-skill guarantees

• Distilled preprocessing for extract_feature_v1.py and extract_feature_v2.py.
• Explicit horizontal-flip TTA behavior and l2_norm normalization.
• Compatibility checks for 512-d face embeddings from IR / IR-SE / ResNet backbones.
• Pairwise verification metrics that expect embeddings in [img1, img2, img3, img4, ...] order.
• Safe wrappers that do not depend on the original checkout path at runtime.