Saon110/bd-crop-vegetable-plant-disease-dataset
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How to use dsett-ml/BengalCropDisease-finetuned-vit with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="dsett-ml/BengalCropDisease-finetuned-vit")
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("dsett-ml/BengalCropDisease-finetuned-vit")
model = AutoModelForImageClassification.from_pretrained("dsett-ml/BengalCropDisease-finetuned-vit")This model is a fine-tuned version of wambugu71/crop_leaf_diseases_vit on Saon110/bd-crop-vegetable-plant-disease-dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4845 | 1.0 | 304 | 0.4700 | 0.8456 |
| 0.3312 | 2.0 | 608 | 0.3128 | 0.8933 |
| 0.2491 | 3.0 | 912 | 0.2703 | 0.9060 |
Base model
WinKawaks/vit-tiny-patch16-224