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---
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)
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