Instructions to use iammartian0/vegetation_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iammartian0/vegetation_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="iammartian0/vegetation_classification_model") 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("iammartian0/vegetation_classification_model") model = AutoModelForImageClassification.from_pretrained("iammartian0/vegetation_classification_model") - Notebooks
- Google Colab
- Kaggle
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README.md
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Model acheived an Top-1 accuracy of 0.929.
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### BibTeX entry and citation info
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Model acheived an Top-1 accuracy of 0.929.
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## Further exploration to do
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- Trainig a multilabel model where model can find if the image is from left side or right side
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on top of classifying the vegetation
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- Fine grained classification of crop labels using Raw/Initial set of street-level images
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### BibTeX entry and citation info
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