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---
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---
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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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---
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# Tiny CNN Classifier for CIFAR-10
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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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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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---
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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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# 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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# 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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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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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. |
