---
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
from transformers import pipeline

# Define the image 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 the model from Hugging Face
pipe = pipeline("image-classification", model="DineshKumar1329/DogCat_Classifier")

# Load and preprocess 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
result = classifier(image_path)

# Extract the predicted label
predicted_label = result[0]['label']

# Output the prediction
print(f'The predicted class for the image is: {predicted_label}')