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: 300 Bytes
bc4546e | 1 2 3 4 5 6 7 8 9 10 11 | {
"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
} |