https://github.com/haseebulhassan437/transferlearning_pre_trained_resnet18

Classify images of ants and bees using transfer learning with a pre-trained ResNet model. This project demonstrates efficient feature extraction and fine-tuning for accurate binary classification.
https://github.com/haseebulhassan437/transferlearning_pre_trained_resnet18

Last synced: 2 months ago
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Classify images of ants and bees using transfer learning with a pre-trained ResNet model. This project demonstrates efficient feature extraction and fine-tuning for accurate binary classification.

• Host: GitHub
• URL: https://github.com/haseebulhassan437/transferlearning_pre_trained_resnet18
• Owner: HaseebUlHassan437
• License: mit
• Created: 2024-08-23T18:38:24.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-08-23T18:41:35.000Z (10 months ago)
• Last Synced: 2024-08-23T20:26:49.275Z (10 months ago)
• Language: Jupyter Notebook
• Size: 561 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# TransferLearning_with_pre_trained_resNet18

**Author: [Haseeb Ul Hassan]**

# Ants and Bees Classification using Transfer Learning

## Project Overview




This repository contains the code and resources for training a convolutional neural network (CNN) to classify images of ants and bees. Transfer learning is utilized to leverage pre-trained models, enabling faster training and improved accuracy.

## Dataset

The dataset is organized into two main folders: train and val.




Training set (train): Contains images used to train the model.

Validation set (val): Contains images used to evaluate the model's performance during training.

Each of these folders has subfolders for the two classes, ants and bees.

## Model Architecture

The project uses a pre-trained model (ResNet) for transfer learning. The final layers are replaced to adapt the model to the binary classification task (ants vs. bees).

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### Key steps:

Feature Extraction: The pre-trained model is used to extract features from the images.

Classification: The final fully connected layers are fine-tuned to classify the images into ants or bees.

To get started, clone the repository and install the necessary dependencies:

## Results

The trained model achieved an accuracy of (given in notebook) on the validation set. Detailed results, including confusion matrices and loss curves, can be found in the notebook.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue for any improvements or suggestions.