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
| { | |
| "split": "val", | |
| "threshold": 0.5, | |
| "n_examples": 3711, | |
| "micro_f1": 0.7099979300351894, | |
| "macro_f1": 0.3058216619253379, | |
| "macro_mAP": 0.44166983994453063, | |
| "n_eval_labels": 212 | |
| } |