Author: [Haseeb Ul Hassan]
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.
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.
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). <
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:
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.
Contributions are welcome! Please feel free to submit a Pull Request or open an Issue for any improvements or suggestions.