---
---
tags:
  - image-classification
  - cnn
  - cifar-10
license: apache-2.0
library_name: pytorch
---

# Tiny CNN Classifier for CIFAR-10

This is a custom **Convolutional Neural Network (CNN)** trained on the **CIFAR-10 dataset**.  
It classifies images into 10 categories:  
`airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck`

---

## 📖 Model Overview

- **Type**: Convolutional Neural Network (CNN)  
- **Architecture**:
  - 2 convolutional layers  
  - 2 max-pooling layers  
  - 2 fully connected layers  
  - ReLU activation functions  
- **Dataset**: CIFAR-10 (10 classes)  
- **Framework**: PyTorch  
- **Test Accuracy**: **69.90%**  
- **Training Epochs**: 5  

The model uses two convolutional layers followed by max-pooling and fully connected layers to classify images. It was trained using Adam optimizer and Cross-Entropy loss.  

---

##  How to Use

> ⚠️ Note: This is **not a Hugging Face Transformers model**.  
> You **cannot** use `pipeline()`. Instead, load it directly with **PyTorch**.

### 1. Clone the repository
```bash
git clone https://huggingface.co/Udayan012/tiny-cnn-classifier
cd tiny-cnn-classifier
```
### 2. Install Dependencies
```bash
pip install torch torchvision pillow
```
### 3. Load the model
```python 
import torch
from model import CustomCNN

# Initialize model and load weights
model = CustomCNN()
model.load_state_dict(torch.load("cnn_model.pth", map_location="cpu"))
model.eval()
```
### 4. Run inference on an image
```python
from torchvision import transforms
from PIL import Image

# CIFAR-10 preprocessing (32x32 RGB)
transform = transforms.Compose([
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
])

# Load an image
img = Image.open("your_image_path.jpg").convert("RGB") 
x = transform(img).unsqueeze(0)

# Predict
with torch.no_grad():
    output = model(x)
    pred_class = output.argmax(1).item()

classes = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']
print("Predicted class:", classes[pred_class])
```

### Training Information

Dataset: CIFAR-10
Optimizer: Adam
Loss Function: Cross-Entropy Loss
Epochs: 5
Batch Size: 32
Learning Rate: 0.001


### Model Limitations

Trained only on CIFAR-10 → works best on 32x32 images with simple backgrounds.
May not generalize well to high-resolution or real-world images.


### License

This model is released under the Apache 2.0 License.
You can freely use, modify, and distribute this model.