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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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tags: |
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- code |
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library_name: transformers |
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--- |
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# ResNet Cat-Dog Classifier |
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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. |
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## Model Details |
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### Architecture: |
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- Backbone: ResNet-18 |
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- Input Size: 128x128 RGB images |
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- Output: Binary classification (Cat or Dog) |
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### Training Details: |
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- Dataset: Kaggle Cats and Dogs dataset |
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- Loss Function: Cross-entropy loss |
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- Optimizer: Adam optimizer |
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- Learning Rate: 0.001 |
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- Epochs: 15 |
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- Batch Size: 32 |
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### Performance: |
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- Accuracy: 90.27% on test images |
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- Training Time: Approximately 1 hour on NVIDIA GTX 1080 Ti |
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## Usage |
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### Installation: |
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- Dependencies: PyTorch, TorchVision, matplotlib |
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### Inference: |
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```python |
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import torch |
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from torchvision import transforms |
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from PIL import Image |
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from transformers import pipeline |
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# Define the image transformation |
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transform = transforms.Compose([ |
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transforms.Resize((128, 128)), |
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transforms.ToTensor(), |
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
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]) |
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# Load the model from Hugging Face |
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pipe = pipeline("image-classification", model="DineshKumar1329/DogCat_Classifier") |
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# Load and preprocess an image |
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image_path = 'path/to/your/image.jpg' |
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image = Image.open(image_path) |
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image = transform(image) |
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image = image.unsqueeze(0) # Add batch dimension |
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# Make a prediction |
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result = classifier(image_path) |
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# Extract the predicted label |
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predicted_label = result[0]['label'] |
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# Output the prediction |
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print(f'The predicted class for the image is: {predicted_label}') |
