yusuf802/leaf-images
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How to use yusuf802/Leaf-Disease-Predictor with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="yusuf802/Leaf-Disease-Predictor")
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("yusuf802/Leaf-Disease-Predictor")
model = AutoModelForImageClassification.from_pretrained("yusuf802/Leaf-Disease-Predictor")This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the leaf-images dataset. It achieves the following results on the evaluation set:
Finetuned model on 66000+ images of different species of leaves along with their diseases
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.9728 | 0.08 | 100 | 0.9026 | 0.8922 |
| 0.4538 | 0.17 | 200 | 0.4412 | 0.9270 |
| 0.2368 | 0.25 | 300 | 0.2870 | 0.9399 |
| 0.2388 | 0.34 | 400 | 0.2208 | 0.9504 |
| 0.1422 | 0.42 | 500 | 0.2046 | 0.9508 |
| 0.1663 | 0.51 | 600 | 0.1538 | 0.9625 |
| 0.1535 | 0.59 | 700 | 0.1427 | 0.9653 |
| 0.1233 | 0.68 | 800 | 0.1133 | 0.9724 |
| 0.1079 | 0.76 | 900 | 0.1005 | 0.9759 |
| 0.1154 | 0.84 | 1000 | 0.0989 | 0.9748 |
| 0.08 | 0.93 | 1100 | 0.0857 | 0.9801 |
Base model
google/vit-base-patch16-224-in21k