Image Classification
Transformers
Safetensors
siglip
multi-label
fashion
apparel
manufacturing
quality-control
siglip2
Instructions to use resoa/garment-attributes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use resoa/garment-attributes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="resoa/garment-attributes") 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") model = AutoModelForImageClassification.from_pretrained("resoa/garment-attributes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b0900f78ce0476ec6c5014378cb45438c7d72bb90238bc1209f5438023d3cb2
- Size of remote file:
- 5.2 kB
- SHA256:
- 859d3d3c32e5e3c6935f7389bbe2c63bdbc8beb4f36d9295068b3c290ccd3b1f
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