Instructions to use rmezapi/sugarcane-diagnosis-swin-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rmezapi/sugarcane-diagnosis-swin-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rmezapi/sugarcane-diagnosis-swin-tiny") 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("rmezapi/sugarcane-diagnosis-swin-tiny") model = AutoModelForImageClassification.from_pretrained("rmezapi/sugarcane-diagnosis-swin-tiny") - Notebooks
- Google Colab
- Kaggle
Sugarcane Leaf Disease Image Classification Model
This model is a fine-tuned version of the microsoft/swin-tiny-patch4-window7-224 model. It is fine-tuned on the Sugarcane Leaf Disease Dataset for 20 epochs using a 85-10-5 split for train, validation, and testing, respectively.
It identifies 5 classes, Healthy leaves, leaves infected with Mosaic disease, Red Rot, Rust disease, and yellow leaf disease.
Try it using this HuggingFace Space!!!
The model achieves ~97% accuracy on testing. Screenshots of data distribution and model performance metrics below.
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Model tree for rmezapi/sugarcane-diagnosis-swin-tiny
Base model
microsoft/swin-tiny-patch4-window7-224



