Instructions to use dacanizalesconvers/material-surface-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dacanizalesconvers/material-surface-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dacanizalesconvers/material-surface-classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dacanizalesconvers/material-surface-classifier") model = AutoModelForImageClassification.from_pretrained("dacanizalesconvers/material-surface-classifier") - timm
How to use dacanizalesconvers/material-surface-classifier with timm:
import timm model = timm.create_model("hf_hub:dacanizalesconvers/material-surface-classifier", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "accuracy": 0.8425, | |
| "f1_macro": 0.735462542106115, | |
| "per_class_f1": { | |
| "asphalt": 0.7906976744186046, | |
| "concrete": 0.7794117647058824, | |
| "metal": 0.5490196078431373, | |
| "other": 0.9070208728652751, | |
| "wood": 0.6511627906976745 | |
| } | |
| } |