This repository demonstrates how to fine-tune a DenseNet-201 deep learning model for image classification using the FastAI library. The core implementation is contained within the finalModel.ipynb Jupyter Notebook.

The primary goal of this project is to showcase transfer learning by adapting a pre-trained DenseNet-201 model (originally trained on ImageNet) to a new dataset. FastAI is utilized to streamline the data loading, model creation, and training loop.

• Model Architecture: DenseNet-201
• Framework: FastAI (PyTorch backend)
• Technique: Transfer Learning (Fine-Tuning)

Follow these instructions to get a copy of the project up and running on your local machine.

• Python 3.6+
• Jupyter Notebook or JupyterLab

1. Clone the repository

   git clone https://github.com/arupa444/Fine-Tuning-Densenet201-using-FastAI.git
   cd Fine-Tuning-Densenet201-using-FastAI

2. Install dependencies It is recommended to use a virtual environment. Install standard data science libraries and FastAI:

   pip install fastai jupyterlab

   (Note: FastAI will automatically install the compatible version of PyTorch)

1. Launch Jupyter Lab:

   jupyter lab

2. Open finalModel.ipynb.
3. Run the notebook cells sequentially to observe the data loading, model setup, and training process.

The notebook covers the following critical steps:

• DataBlock API: Flexible data loading and augmentation pipelines.
• Vision Learner: Initializing densenet201 with pretrained weights.
• lr_find(): FastAI's utility to find the optimal learning rate.
• fine_tune(): The one-cycle training policy for efficient transfer learning.
• Interpretation: Using ClassificationInterpretation to view confusion matrices and top losses.

Contributions, issues, and feature requests are welcome!

This project is open-source.