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
File size: 190 Bytes
bc4546e | 1 2 3 4 5 6 7 8 9 | {
"split": "val",
"threshold": 0.5,
"n_examples": 3711,
"micro_f1": 0.7099979300351894,
"macro_f1": 0.3058216619253379,
"macro_mAP": 0.44166983994453063,
"n_eval_labels": 212
} |