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
File size: 250 Bytes
9a43f3b | 1 2 3 4 5 6 7 8 9 10 11 | {
"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
}
} |