Instructions to use OttoYu/TreeDisease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OttoYu/TreeDisease with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OttoYu/TreeDisease") 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("OttoYu/TreeDisease") model = AutoModelForImageClassification.from_pretrained("OttoYu/TreeDisease", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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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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co2_eq_emissions:
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emissions: 0.9533671404013373
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# Model Trained Using AutoTrain
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- Problem type: Multi-class Classification
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- Model ID: 43309110181
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- CO2 Emissions (in grams): 0.9534
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## Validation Metrics
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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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## Validation Metrics
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