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: 488 Bytes
9a43f3b | 1 2 3 4 5 6 7 8 9 10 11 12 | precision recall f1-score support
asphalt 0.7391 0.8500 0.7907 20
concrete 0.6974 0.8833 0.7794 60
metal 0.4516 0.7000 0.5490 20
other 0.9676 0.8536 0.9070 280
wood 0.6087 0.7000 0.6512 20
accuracy 0.8425 400
macro avg 0.6929 0.7974 0.7355 400
weighted avg 0.8719 0.8425 0.8514 400
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