Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+  - image-classification
+  - computer-vision
+  - vegetables
+  - pytorch
+  - food
+datasets:
+  - Custom
+metrics:
+  - accuracy
+  - confusion_matrix
+model-index:
+  - name: VeggieNet
+    results:
+      - task:
+          type: image-classification
+          name: Image Classification
+        dataset:
+          name: Custom Vegetable Dataset
+          type: image
+        metrics:
+          - type: accuracy
+            value: 91.63%
+          - type: confusion_matrix
+            value: included
+---
+
+# 🥕 VeggieNet: Vegetable Image Classifier
+
+**VeggieNet** is a deep learning model trained in PyTorch for classifying vegetable images into categories like tomato, carrot, potato, etc. It uses a fully connected neural network with regularization (BatchNorm and Dropout) to prevent overfitting and improve generalization.
+
+## 🧠 Model Architecture
+
+The network takes 128x128 RGB images and passes them through the following layers:
+
+```python
+nn.Sequential(
+    nn.Flatten(),
+    nn.Linear(3 * 128 * 128, 512),
+    nn.BatchNorm1d(512),
+    nn.ReLU(),
+    nn.Dropout(0.3),
+    nn.Linear(512, 256),
+    nn.BatchNorm1d(256),
+    nn.ReLU(),
+    nn.Dropout(0.3),
+    nn.Linear(256, 128),
+    nn.BatchNorm1d(128),
+    nn.ReLU(),
+    nn.Dropout(0.3),
+    nn.Linear(128, num_classes)
+)
+```
+
+- **Loss Function**: `CrossEntropyLoss`
+- **Optimizer**: `Adam`
+- **Input Size**: `3x128x128`
+- **Output**: `num_classes` (one per vegetable category)
+
+## 📂 Dataset
+
+This model is trained on a custom dataset from kaggle of vegetable images organized into:
+
+```
+vegetables_dataset/
+├── train/
+├── val/
+└── test/
+```
+
+Each subfolder represents a vegetable class (e.g., `carrot/`, `tomato/`, etc.). To download [Click Here](https://www.kaggle.com/datasets/misrakahmed/vegetable-image-dataset?select=Vegetable+Images)
+
+## 📈 Training & Evaluation
+
+- Trained for **10 epochs**
+- Batch size: 16
+- Includes validation + test evaluation
+- Final accuracy on test set: **~91.63%**
+- Confusion matrix is included in the evaluation
+
+## ✅ Intended Use
+
+- Educational projects
+- Computer vision experiments
+- Simple food classification tasks
+
+## 🚫 Limitations
+
+- Not robust to background noise or very similar vegetables
+- May underperform on unseen real-world data if distribution differs
+
+## 💡 Future Improvements
+
+- Replace FC layers with a CNN for better spatial feature learning
+- Use transfer learning (e.g., ResNet18)
+- Increase dataset diversity and quantity
+
+## 📜 License
+
+This model is available under the **Apache-2.0 License**.
+
+## ✍️ Author
+
+- Created by: *Arun  Arunisto*
+- GitHub: [arun-arunisto](https://github.com/arun-arunisto)