LogSAD
[CVPR 2025] Towards Training-free Anomaly Detection with Vision and Language Foundation Models
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- __init__.py
- callback.py
- utils.py
- __init__.py
- base.py
- min_max_normalization.py
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- anomaly_map.py
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- torch_model.py
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- anomaly_map.py
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- torch_model.py
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- torch_model.py
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- lightning_model.py
- loss.py
- README.md
- torch_model.py
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- lightning_model.py
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- torch_model.py
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- torch_model.py
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- torch_model.py
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- loss.py
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- statistic_scores_model_ensemble_few_shot_val.pkl
- statistic_scores_model_ensemble_val.pkl
- coca_base.json
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- vit_medium_patch16_gap_256.json
- vit_relpos_medium_patch16_cls_224.json
- xlm-roberta-base-ViT-B-32.json
- xlm-roberta-large-ViT-H-14.json
- __init__.py
- big_vision.py
- bpe_simple_vocab_16e6.txt.gz
- coca_model.py
- constants.py
- factory.py
- hf_configs.py
- hf_model.py
- loss.py
- model.py
- modified_resnet.py
- openai.py
- pos_embed.py
- pretrained.py
- push_to_hf_hub.py
- timm_model.py
- tokenizer.py
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- transformer_raw.py
- transformer_sclip.py
- utils.py
- version.py
- zero_shot_classifier.py
- zero_shot_metadata.py
- compute_coreset.py
- evaluation.py
- imagenet_template.py
- model_ensemble.py
- model_ensemble_few_shot.py
- prompt_ensemble.py
- README.md
- requirements.txt
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