Image Classification
Transformers
Safetensors
vit
vision
STEM-AI-mtl/City_map
Google
ViT
STEM-AI-mtl
Instructions to use STEM-AI-mtl/City_map-vit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use STEM-AI-mtl/City_map-vit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="STEM-AI-mtl/City_map-vit-base-patch16-224") 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("STEM-AI-mtl/City_map-vit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("STEM-AI-mtl/City_map-vit-base-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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- STEM-AI-mtl
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datasets:
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- STEM-AI-mtl/City_map
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widget:
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example_title: Teapot
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
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example_title: Palace
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metrics:
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- accuracy
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---
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- STEM-AI-mtl
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datasets:
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- STEM-AI-mtl/City_map
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widget:
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- image: https://cdn.britannica.com/50/69550-050-B9DA3DCA/Central-New-York-City-borough-Manhattan-Park.jpg
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output:
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text: NYC
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metrics:
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- accuracy
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