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
| { | |
| "eval_loss": 0.03274541348218918, | |
| "eval_micro_f1": 0.7099979300351894, | |
| "eval_macro_f1": 0.3058216619253379, | |
| "eval_macro_mAP": 0.4416697901042945, | |
| "eval_n_eval_labels": 212, | |
| "eval_runtime": 24.8801, | |
| "eval_samples_per_second": 149.155, | |
| "eval_steps_per_second": 4.662, | |
| "epoch": 4.0 | |
| } |