metadata

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:

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}')