VGG-image-classification
This project is focused on developing a VGG deep learning model to classify images
VGG-image-classification
Indian Bird Species Classification using CNN This project is focused on developing a deep learning model to classify images of Indian bird species into 25 distinct classes. The dataset comprises 30,000 training images and 7,500 validation images, representing different bird species commonly found in India.
Dataset
The dataset contains a total of 37,000 images split into train and validation sets in an 80:20 ratio, with 30,000 images in the training set and 7,500 images in the validation set. Each species has 1,500 images in the dataset. This dataset can be used for image classification tasks and to develop machine learning models to recognize different species of birds found in India. Download the Dataset You can download the dataset from the following link: Indian Birds Species Image Classification Dataset
Project Structure
- Dataset: The dataset is organized into training and validation sets, each containing images categorized into 25 classes.
- Model: A VGG model was implemented using TensorFlow and Keras. The VGG architecture consists of a series of convolutional layers followed by max pooling layers. This architecture is known for its simplicity and effectiveness, and transfer learning can be employed by using a pre-trained VGG model.
- Data Augmentation: Data augmentation techniques such as random flipping, rotation, and zooming were applied to enhance model generalization.
- Optimization: The model was compiled with the Adam optimizer and trained with early stopping and checkpointing to save the best-performing model.
- Evaluation: The model's performance was evaluated based on accuracy and loss on the validation set.
Key Features
- VGG Architecture: Utilized the VGG architecture for its proven performance in image classification tasks.
- Data Augmentation: Integrated data augmentation to improve model robustness.
- Training Optimization: Early stopping and model checkpointing to ensure optimal training and prevent overfitting.
- TensorBoard: Visualization of training progress using TensorBoard.