Update README.md

README.md

@@ -1,28 +1,69 @@
 ---
 language: en
+license: mit
 tags:
 - pytorch
 - plant-disease
 - image-classification
+- agriculture
+- computer-vision
 datasets:
-- plant-village-dataset
+- plant-village
 ---
 
 # Plant Disease Classification Model
 
-This model classifies plant diseases from the PlantVillage dataset. It was trained on:
-- Apple
-- Tomato
-- Corn (Maize)
+🌱 EfficientNet-B2 based model for classifying plant diseases in apples, tomatoes, and corn (maize).
 
 ## Model Details
-- Architecture: EfficientNet-B2 with custom head
-- Training Accuracy: {your_train_accuracy}%
-- Validation Accuracy: {your_val_accuracy}%
-- Test Accuracy: {your_test_accuracy}%
+
+### Architecture
+- **Backbone**: EfficientNet-B2 (pretrained)
+- **Custom Head**: 
+  - Attention mechanism
+  - 3 dense layers (512, 256, num_classes)
+  - Dropout regularization (0.3)
+
+### Training Data
+- **Dataset**: PlantVillage Dataset
+- **Classes**: 14 total (4 Apple, 6 Tomato, 4 Corn diseases + healthy)
+- **Train/Val/Test Split**: 80%/10%/10%
+- **Image Size**: 224x224
+
+### Performance Metrics
+| Metric       | Value   |
+|--------------|---------|
+| Train Accuracy | 98.66% |
+| Val Accuracy   | 99.24% |
+| Test Accuracy  | 98.91% |
 
 ## How to Use
+
+### Inference
 ```python
 from transformers import AutoModelForImageClassification
+from PIL import Image
+import torch
+import torchvision.transforms as transforms
+
+# Load model
+model = AutoModelForImageClassification.from_pretrained("Abuzaid01/plant-disease-classifier")
+
+# Preprocess image
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Load and transform image
+image = Image.open("plant.jpg")
+inputs = transform(image).unsqueeze(0)
+
+# Predict
+with torch.no_grad():
+    outputs = model(inputs)
+    prediction = torch.argmax(outputs.logits, dim=1).item()
 
-model = AutoModelForImageClassification.from_pretrained("{Abuzaid01}/plant-disease-classifier")
+print(f"Predicted class: {model.config.id2label[prediction]}")