Transfer Learning with VGG19 for Image Classification This repository contains the implementation of a Convolutional Neural Network (CNN) model for image classification using transfer learning with VGG19 architecture. Transfer learning is a technique where a pre-trained model is used as a starting point and then fine-tuned on a specific task. In this case, we leverage the VGG19 model, pre-trained on the ImageNet dataset, to classify images into predefined classes.
Requirements Python 3.x TensorFlow 2.x Keras NumPy Matplotlib (optional for visualization) Dataset The model is trained and evaluated on the ImageNet dataset, which is a large-scale dataset consisting of millions of labeled images across thousands of categories. The dataset is widely used for training and benchmarking deep learning models.
Model Architecture The model architecture is based on the VGG19 architecture, which is a deep convolutional neural network consisting of 19 layers, including convolutional layers, pooling layers, and fully connected layers. The pre-trained VGG19 model is loaded and the fully connected layers are replaced with custom layers suitable for the target task.
Usage Clone the repository:
bash Copy code git clone https://github.com/garvkhurana/transfer-learning-vgg19.git Install the required dependencies:
bash Copy code pip install -r requirements.txt Train the model:
bash Copy code python train.py Evaluate the model:
bash Copy code python evaluate.py Results The model achieves an accuracy of [insert accuracy here] on the validation set, demonstrating its effectiveness in classifying images using transfer learning with VGG19 architecture.
The dataset link is: https://drive.google.com/drive/folders/1vdr9CC9ChYVW2iXp6PlfyMOGD-4Um1ue
eployed the project on the local server too with the help of flak library of PYTHON code is in the app.py file