| ---
|
| ---
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| tags:
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| - image-classification
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| - cnn
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| - cifar-10
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| license: apache-2.0
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| library_name: pytorch
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| ---
|
|
|
| # Tiny CNN Classifier for CIFAR-10
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|
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| This is a custom **Convolutional Neural Network (CNN)** trained on the **CIFAR-10 dataset**.
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| It classifies images into 10 categories:
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| `airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck`
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|
|
| ---
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|
|
| ## 📖 Model Overview
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|
|
| - **Type**: Convolutional Neural Network (CNN)
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| - **Architecture**:
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| - 2 convolutional layers
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| - 2 max-pooling layers
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| - 2 fully connected layers
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| - ReLU activation functions
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| - **Dataset**: CIFAR-10 (10 classes)
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| - **Framework**: PyTorch
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| - **Test Accuracy**: **69.90%**
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| - **Training Epochs**: 5
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|
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| 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.
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|
|
| ---
|
|
|
| ## How to Use
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|
|
| > ⚠️ Note: This is **not a Hugging Face Transformers model**.
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| > You **cannot** use `pipeline()`. Instead, load it directly with **PyTorch**.
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|
|
| ### 1. Clone the repository
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| ```bash
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| git clone https://huggingface.co/Udayan012/tiny-cnn-classifier
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| cd tiny-cnn-classifier
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| ```
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| ### 2. Install Dependencies
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| ```bash
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| pip install torch torchvision pillow
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| ```
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| ### 3. Load the model
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| ```python
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| import torch
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| from model import CustomCNN
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|
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| # Initialize model and load weights
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| model = CustomCNN()
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| model.load_state_dict(torch.load("cnn_model.pth", map_location="cpu"))
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| model.eval()
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| ```
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| ### 4. Run inference on an image
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| ```python
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| from torchvision import transforms
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| from PIL import Image
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|
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| # CIFAR-10 preprocessing (32x32 RGB)
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| transform = transforms.Compose([
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| transforms.Resize((32, 32)),
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| transforms.ToTensor(),
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| ])
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|
|
| # Load an image
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| img = Image.open("your_image_path.jpg").convert("RGB")
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| x = transform(img).unsqueeze(0)
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|
|
| # Predict
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| with torch.no_grad():
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| output = model(x)
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| pred_class = output.argmax(1).item()
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|
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| classes = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']
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| print("Predicted class:", classes[pred_class])
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| ```
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|
|
| ### Training Information
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|
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| Dataset: CIFAR-10
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| Optimizer: Adam
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| Loss Function: Cross-Entropy Loss
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| Epochs: 5
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| Batch Size: 32
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| Learning Rate: 0.001
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|
|
|
|
| ### Model Limitations
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|
|
| Trained only on CIFAR-10 → works best on 32x32 images with simple backgrounds.
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| May not generalize well to high-resolution or real-world images.
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|
|
|
|
| ### License
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|
|
| This model is released under the Apache 2.0 License.
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| You can freely use, modify, and distribute this model. |
