An updated version of this project has been uploaded to the https://github.com/embisht/transfer_learning_VGG16 repository.
This repository contains the code for a binary classification project using transfer learning with the VGG-16 model. The project leverages MLflow for experiment tracking and management. Transfer learning is a powerful technique in deep learning where a pre-trained model on a large dataset is fine-tuned for a specific task. In this project, we use the VGG-16 (Visual Geometry Group) model, which is a widely used convolutional neural network (CNN) architecture. After training, the model's performance metrics and experiment details can be visualized through the MLflow UI. This includes accuracy, loss, and other relevant metrics.
Requirements Python 3.x TensorFlow Keras MLflow Other dependencies can be installed using the requirements.txt file.