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README.md

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----
-license: mit
----
+---
+language: en
+license: mit
+tags:
+- pytorch
+- efficientnet
+- image-classification
+- rice-disease
+datasets:
+- Subh775/Rice-Disease-Augmented
+metrics:
+- accuracy
+---
+
+# EfficientNet-B3 for Rice Disease Classification
+
+This model is fine-tuned on the Rice Disease Augmented dataset to classify different rice diseases.
+
+## Classes
+[0, 1, 2, 3]
+
+## Usage
+
+```python
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+import torch
+from PIL import Image
+
+# Load the model and feature extractor
+model = AutoModelForImageClassification.from_pretrained("your-username/rice-disease-efficientnet-b3")
+feature_extractor = AutoFeatureExtractor.from_pretrained("your-username/rice-disease-efficientnet-b3")
+
+# Load an image
+image = Image.open("path/to/image.jpg")
+
+# Preprocess the image
+inputs = feature_extractor(images=image, return_tensors="pt")
+
+# Get predictions
+with torch.no_grad():
+    logits = model(**inputs).logits
+    predicted_class_idx = logits.argmax(-1).item()
+
+# Convert to class name
+class_names = [0, 1, 2, 3]
+print(f"Predicted class: {class_names[predicted_class_idx]}")
+```

config.json

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+{
+  "architectures": [
+    "EfficientNetForImageClassification"
+  ],
+  "model_type": "efficientnet",
+  "num_labels": 4,
+  "id2label": {0: 0, 1: 1, 2: 2, 3: 3},
+  "label2id": {0: 0, 1: 1, 2: 2, 3: 3}
+}

model.onnx

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+size 42774296

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_resize": true,
+  "image_processor_type": "EfficientNetImageProcessor",
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 2,
+  "size": {
+    "height": 300,
+    "width": 300
+  }
+}

pytorch_model.bin

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