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---
license: mit
datasets:
- jxie/stl10
---
# Image Classifier
This repository contains a pre-trained PyTorch model, designed for classifying images into 10 categories: airplane, bird, car, cat, deer, dog, horse, monkey, ship, and truck. The model uses a Convolutional Neural Network (CNN) architecture and can classify images based on the categories below.
## Model Overview
The model is a simple CNN classifier with two convolutional blocks followed by a fully connected layer. It was trained on an image dataset and can classify images into the following categories:
- **0**: Airplane
- **1**: Bird
- **2**: Car
- **3**: Cat
- **4**: Deer
- **5**: Dog
- **6**: Horse
- **7**: Monkey
- **8**: Ship
- **9**: Truck
## Model Architecture
The model consists of the following layers:
1. **Conv Block 1**: Two convolutional layers with ReLU activations followed by max pooling.
2. **Conv Block 2**: Two more convolutional layers with ReLU activations and max pooling.
3. **Fully Connected Classifier**: A linear layer that maps the features to 10 output categories.
Here’s the architecture of the model:
```python
class CNNV0(nn.Module):
def __init__(self, input_shape: int, hidden_units: int, output_shape: int):
super().__init__()
self.conv_block_1 = nn.Sequential(
nn.Conv2d(in_channels=input_shape, out_channels=hidden_units, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=hidden_units, out_channels=hidden_units, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2)
)
self.conv_block_2 = nn.Sequential(
nn.Conv2d(in_channels=hidden_units, out_channels=hidden_units, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=hidden_units, out_channels=hidden_units, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2)
)
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(in_features=hidden_units*576, out_features=output_shape)
)
def forward(self, x):
x = self.conv_block_1(x)
x = self.conv_block_2(x)
x = self.classifier(x)
return x
```
## Requirements
- **Python** 3.7 or higher
- **PyTorch** 1.8 or higher
- **torchvision** (for loading and preprocessing images)
## Usage
1. Clone this repository and install dependencies:
```bash
git clone <repository-url>
cd <repository-folder>
pip install torch torchvision
```
2. Load and use the model in your Python script:
```python
import torch
from torchvision import transforms
from PIL import Image
# Load the model
model = torch.load('model_0.pth')
model.eval() # Set to evaluation mode
# Load and preprocess the image
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
img = Image.open('path_to_image.jpg')
img = transform(img).view(1, 3, 224, 224) # Reshape to (1, 3, 224, 224) for batch processing
# Predict
with torch.no_grad():
output = model(img)
_, predicted = torch.max(output, 1)
print("Predicted Aircraft Type:", predicted.item())
```
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