Instructions to use edixo/road_good_damaged_condition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edixo/road_good_damaged_condition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="edixo/road_good_damaged_condition") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("edixo/road_good_damaged_condition") model = AutoModelForImageClassification.from_pretrained("edixo/road_good_damaged_condition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from edixo/road_good_damaged_condition: direct link, hf CLI and curl.
- Browser
- Download file 792 Bytes
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https://huggingface.co/edixo/road_good_damaged_condition/resolve/main/README.md
- Command line
-
hf download hf://edixo/road_good_damaged_condition/README.md
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curl -L -o README.md https://huggingface.co/edixo/road_good_damaged_condition/resolve/main/README.md
792 Bytes
metadata
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: road_good_damaged_condition
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9583333134651184
road_good_damaged_condition
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.

