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resoa
/
garment-attributes-v2

Image Classification
Transformers
Safetensors
siglip
multi-label
fashion
apparel
siglip2
augmentation
Model card Files Files and versions
xet
Community

Instructions to use resoa/garment-attributes-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use resoa/garment-attributes-v2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-classification", model="resoa/garment-attributes-v2")
    pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForImageClassification
    
    processor = AutoProcessor.from_pretrained("resoa/garment-attributes-v2")
    model = AutoModelForImageClassification.from_pretrained("resoa/garment-attributes-v2", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
garment-attributes-v2
372 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
resoajoe's picture
resoajoe
add cheap-baseline comparison (7.6x) and measured failure modes -- card audit
bb03c55 verified 15 days ago
  • .gitattributes
    1.52 kB
    initial commit 16 days ago
  • README.md
    6.98 kB
    add cheap-baseline comparison (7.6x) and measured failure modes -- card audit 15 days ago
  • config.json
    20.4 kB
    v2: crop-scale augmentation, +11.4% macro-mAP, ablated (jitter 61% / padding 39%) 16 days ago
  • label_space.json
    34.9 kB
    v2: crop-scale augmentation, +11.4% macro-mAP, ablated (jitter 61% / padding 39%) 16 days ago
  • model.safetensors
    372 MB
    xet
    v2: crop-scale augmentation, +11.4% macro-mAP, ablated (jitter 61% / padding 39%) 16 days ago
  • per_attribute.json
    22.1 kB
    v2: crop-scale augmentation, +11.4% macro-mAP, ablated (jitter 61% / padding 39%) 16 days ago
  • per_attribute_v2.json
    34.4 kB
    v2: crop-scale augmentation, +11.4% macro-mAP, ablated (jitter 61% / padding 39%) 16 days ago
  • preprocessor_config.json
    354 Bytes
    v2: crop-scale augmentation, +11.4% macro-mAP, ablated (jitter 61% / padding 39%) 16 days ago
  • train_meta.json
    126 Bytes
    v2: crop-scale augmentation, +11.4% macro-mAP, ablated (jitter 61% / padding 39%) 16 days ago