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feature-extraction

feature-extraction

Route Torchreid feature extraction, model discovery/building, checkpoint loading, distance scoring, rank evaluation, re-ranking, model complexity, ranked-result visualization, and activation-map visualization.

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Documentation de la Skill

GitHub

name: feature-extraction description: "Route Torchreid feature extraction, model discovery/building, checkpoint loading, distance scoring, rank evaluation, re-ranking, model complexity, ranked-result visualization, and activation-map visualization." disable-model-invocation: true metadata: disco-role: operating license: MIT

Feature Extraction

Use this sub-skill when the task mentions any of the following:

  • FeatureExtractor, embeddings, or extracting features from images, numpy arrays, or tensors
  • comparing query and gallery sets
  • show_avai_models or build_model
  • load_pretrained_weights or local checkpoint loading
  • compute_distance_matrix, evaluate_rank, or re_ranking
  • compute_model_complexity
  • visualize_ranked_results or visrank
  • activation maps / visactmap / return_featuremaps=True

Route elsewhere

  • Training, data managers, Engine.run, or dataset setup → training-evaluation
  • ONNX / OpenVINO / TFLite export → model-export
  • DML, OSNet-AIN NAS, or PA-100K attribute-recognition projects are excluded long-tail gaps unless a future extension bundles those project sources

Start here

Bundled helpers

Core rules

  • Prefer local checkpoint paths. Do not rely on automatic pretrained downloads in helper scripts.
  • FeatureExtractor returns a tensor shaped (B, D) and accepts image paths, HWC numpy arrays, or torch.Tensor inputs.
  • compute_distance_matrix returns squared Euclidean distances for metric='euclidean'.
  • evaluate_rank consumes NumPy distance matrices plus pid/camid arrays and raises if none of the query identities appear in the gallery.
  • Activation-map visualization only works when the model forward() accepts return_featuremaps=True and returns 4-D feature maps at eval time.
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