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name: evaluation description: "Evaluate Facenet embeddings with LFW pair files, ROC/VAL/FAR metrics, and fixed-standardization checks." metadata: disco-role: operating disable-model-invocation: true license: MIT

Facenet Evaluation

Use this sub-skill when the task involves validate_on_lfw, LFW pairs, face verification metrics, training-time LFW validation, or interpreting Facenet accuracy/VAL/AUC/EER output.

When to read

• The user wants to run or debug LFW validation.
• A pair file has skipped pairs, missing images, or unexpected fold behavior.
• The task mentions distance_metric, subtract_mean, use_flipped_images, use_fixed_image_standardization, ROC, validation rate, FAR, AUC, or EER.
• The user asks how to compare new model accuracy with README pretrained model numbers.

Inputs and prerequisites

• An aligned LFW-style directory where each identity has files such as Name/Name_0001.jpg or .png.
• A pairs file with same/different identity rows. Validate it with scripts/validate_lfw_pairs.py.
• A model path accepted by Facenet. Read ../model-export-and-checkpoints/SKILL.md if model loading fails.
• A TensorFlow 1.x Facenet environment.

Workflow

1. Validate the LFW directory and pairs file.
2. Build a validation command with scripts/build_lfw_validation_command.py.
3. For 2018 README models, include --use_fixed_image_standardization unless evidence shows the model expects per-image standardization.
4. Ensure 2 * number_of_pairs * number_of_flips is divisible by --lfw_batch_size; the source script asserts this.
5. Run validation in a bounded environment and capture accuracy, validation rate, AUC, and EER lines.
6. Read references/metrics-reference.md before comparing thresholds or accuracy across datasets.

Common command shape

python scripts/build_lfw_validation_command.py LFW_ALIGNED_DIR MODEL_PATH --lfw-pairs pairs.txt --lfw-batch-size 100 --lfw-nrof-folds 10 --use-fixed-image-standardization

The builder emits a module-style validate_on_lfw command.

Record the result

Always record the model path, checkpoint/frozen-graph form, pairs file, aligned-data root, image size, batch size, fold count, distance metric, mean subtraction, flip setting, fixed-standardization setting, and the printed accuracy/VAL/FAR/AUC/EER values. Without these fields, a later run is not comparable to the README benchmark.

Related routes

• Dataset alignment and pair-file layout: ../data-and-alignment/SKILL.md.
• Training scripts that run LFW validation each epoch: ../training/SKILL.md.
• Frozen graph/checkpoint path issues: ../model-export-and-checkpoints/SKILL.md.

Troubleshooting

Read references/troubleshooting.md for skipped pairs, batch-size assertions, fixed-standardization mismatch, insufficient folds, and metric interpretation pitfalls.