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---
license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- code
library_name: transformers
---
# ResNet Cat-Dog Classifier
This repository contains a ResNet-based convolutional neural network trained to classify images as either cats or dogs. The model achieves an accuracy of 90.27% on a test dataset and is fine-tuned using transfer learning on the ImageNet dataset. It uses PyTorch for training and inference.
## Model Details
### Architecture:
- Backbone: ResNet-18
- Input Size: 128x128 RGB images
- Output: Binary classification (Cat or Dog)
### Training Details:
- Dataset: Kaggle Cats and Dogs dataset
- Loss Function: Cross-entropy loss
- Optimizer: Adam optimizer
- Learning Rate: 0.001
- Epochs: 15
- Batch Size: 32
### Performance:
- Accuracy: 90.27% on test images
- Training Time: Approximately 1 hour on NVIDIA GTX 1080 Ti
## Usage
### Installation:
- Dependencies: PyTorch, TorchVision, matplotlib
### Inference:
```python
import torch
from torchvision import transforms
from PIL import Image
# Load the model
model = torch.hub.load('your-username/your-repository', 'resnet_cat_dog_classifier')
# Define the transformation
transform = transforms.Compose([
transforms.Resize((128, 128)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# Load an image
image_path = 'path/to/your/image.jpg'
image = Image.open(image_path)
image = transform(image)
image = image.unsqueeze(0) # Add batch dimension
# Make a prediction
model.eval()
with torch.no_grad():
outputs = model(image)
temp, predicted = torch.max(outputs, 1)
# Output the prediction
print(f'The predicted class for the image is: {"Cat" if predicted.item() == 0 else "Dog"}')
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