算法艺术创作
algorithmic-art
通过代码和可调参数创作生成艺术与交互式视觉效果。
feature-extraction【设计与创意】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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Use this sub-skill when the task mentions any of the following:
FeatureExtractor, embeddings, or extracting features from images, numpy arrays, or tensorsshow_avai_models or build_modelload_pretrained_weights or local checkpoint loadingcompute_distance_matrix, evaluate_rank, or re_rankingcompute_model_complexityvisualize_ranked_results or visrankvisactmap / return_featuremaps=TrueEngine.run, or dataset setup → training-evaluationFeatureExtractor 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.forward() accepts return_featuremaps=True and returns 4-D feature maps at eval time.