Instructions to use andupets/real-estate-image-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andupets/real-estate-image-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="andupets/real-estate-image-classification") 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("andupets/real-estate-image-classification") model = AutoModelForImageClassification.from_pretrained("andupets/real-estate-image-classification") - Notebooks
- Google Colab
- Kaggle
real-estate-image-classification
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
bathroom
bedroom
dining room
house facade
kitchen
living room
sao paulo apartment facade
- Downloads last month
- 423
Spaces using andupets/real-estate-image-classification 11
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Nuno-Tome/simple_image_classifier
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Nuno-Tome/compared_image_classifier
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Dannel/gender
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jstranik/andupets-real-estate-image-classification
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jeanbaptdzd/andupets-real-estate-image-classification
Evaluation results
- Accuracyself-reported0.896






