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name: facenet description: "Use davidsandberg/facenet for TensorFlow 1.x face recognition workflows: MTCNN alignment, embeddings, classification, LFW evaluation, training, and model export." metadata: disco-role: operating disable-model-invocation: true license: MIT

Facenet Repo Skill

Use this skill when a task involves David Sandberg's TensorFlow 1.x FaceNet repository: aligning face images with MTCNN, extracting embeddings, comparing faces, training an SVM classifier, evaluating on LFW pairs, training FaceNet models, or exporting checkpoints to frozen graphs.

First checks

1. Treat Facenet as a source-style TensorFlow 1.x project, not a modern packaged library with console entry points.
2. Prefer a Python environment that can import TensorFlow 1.x APIs (tf.Session, tf.Graph, tf.train, and tf.contrib.slim). TensorFlow 2.x without compatibility shims is not enough.
3. Ensure Facenet modules are importable as modules such as facenet, lfw, compare, classifier, train_softmax, train_tripletloss, freeze_graph, align.detect_face, and models.inception_resnet_v1.
4. Run scripts/check_facenet_environment.py to verify imports, TensorFlow version, and common dependency availability before planning a workflow.
5. Read references/compatibility-and-install.md when installing or debugging the old TensorFlow/Python dependency stack.

Install/inspect

For faithful historical execution, use the repository's documented TensorFlow 1.7/Python 2.7 or 3.5 environment. On modern hosts, use an isolated Python 3.7-era environment with a compatible TensorFlow 1.x build, NumPy/SciPy/scikit-learn/OpenCV/h5py/Pillow, and a compatible protobuf<3.20; do not install into the interpreter running the agent. Because this repo has no packaging metadata, make its source modules importable before running module-style commands. The root checker is the minimum read-only verification:

python scripts/check_facenet_environment.py --json

Route by task

• Dataset layout, image preprocessing, and MTCNN alignment: read sub-skills/data-and-alignment/SKILL.md for class-folder datasets, pairs.txt basics, prewhiten/crop/flip behavior, and alignment command construction.
• Embeddings, comparison, classifiers, and clustering: read sub-skills/embeddings-and-classification/SKILL.md for model-backed embedding extraction, compare, SVM classifier, .npy exports, DBSCAN, and contributed recognition examples.
• LFW and verification metrics: read sub-skills/evaluation/SKILL.md for validate_on_lfw, pair-file validation, ROC/VAL/FAR/AUC/EER interpretation, and fixed-standardization warnings.
• Training and model definitions: read sub-skills/training/SKILL.md for softmax/triplet training commands, model definitions, loss functions, learning-rate schedules, logging, and checkpoint outputs.
• Model files, checkpoints, and frozen graphs: read sub-skills/model-export-and-checkpoints/SKILL.md for accepted model path formats, load_model, checkpoint selection, tensor names, and freeze_graph workflows.

Common Facenet objects and conventions

• A class-folder dataset is data_dir/person_name/image.ext; labels are assigned by sorted directory order.
• The main embedding tensor is usually embeddings:0, input images are input:0, and inference mode is controlled by phase_train:0.
• A checkpoint model directory must contain exactly one .meta file plus checkpoint state or model-*.ckpt-* files.
• A frozen graph is a .pb file that facenet.load_model imports directly.
• Pretrained models named in the README commonly use image size 160; newer 2018 models require fixed image standardization for comparable LFW results.

Default handoff order

For a new end-to-end request, use this order unless the user already supplies validated artifacts:

1. Run the environment checker and confirm TF1 imports.
2. Validate the class-folder or LFW data layout.
3. Inspect the checkpoint directory or frozen graph signature.
4. Build the narrowest workflow command with the owning sub-skill helper.
5. Run a bounded help/fixture/model smoke before any long, networked, or hardware-interactive job.
6. Record output directories, model path, image size, standardization mode, batch size, and seed so later comparisons are reproducible.

If a request crosses multiple routes, keep each artifact explicit: raw data, aligned data, model/checkpoint, classifier pickle, embeddings, metrics, and logs should not be silently mixed.

Safety and scope

• Full training, LFW benchmarks, and pretrained comparison usually require external datasets or model downloads. Treat them as skip-network or skip-expensive unless the user supplies assets and approves runtime cost.
• Webcam recognition in contributed scripts needs a camera/display plus hard-coded model/classifier assumptions. Document it or adapt it carefully; do not start an infinite webcam loop as a smoke test.
• Do not use TensorFlow 2.x behavior to infer Facenet correctness without checking TF1 compatibility.
• The generated skill excludes the experimental VAE/generative branch and broad tmp/ experiments unless the user explicitly asks for those historical utilities.

References

• references/repo-provenance.md records the source commit, evidence paths, and generation baseline.
• references/repo-routing-metadata.json is structured metadata for managed repo-skill routing.
• references/troubleshooting.md covers cross-cutting install/import/model/data failures.