Instructions to use mmgyorke/vit-world-landmarks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmgyorke/vit-world-landmarks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mmgyorke/vit-world-landmarks") 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("mmgyorke/vit-world-landmarks") model = AutoModelForImageClassification.from_pretrained("mmgyorke/vit-world-landmarks") - Notebooks
- Google Colab
- Kaggle
vit-world-landmarks
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.
Example Images
arc de triomphe
big ben
la sagrada familia
leaning tower of pisa
taj mahal
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Evaluation results
- Accuracyself-reported1.000




