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shashankag14 / fine-tune-object-detector

A step-by-step PyTorch guide for improved vehicle type 2D object detection based on Faster-RCNN using transfer learning and data augmentation.
MIT License
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faster-rcnn fine-tuning object-detection transfer-learning

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Fine-Tuning a Faster R-CNN for Object Detection

Fine-tuning a Faster R-CNN object detection model using PyTorch for improved object detection accuracy. This repository provides a Jupyter Notebook that takes you through the steps of re-training a pre-trained model on a custom dataset, performing data augmentation, and evaluating the model's performance.

Introduction

In this project, I have fine-tuned a Faster R-CNN model for object detection using a custom dataset. Faster R-CNN is a state-of-the-art object detection algorithm that combines deep learning with region proposal networks. By utilizing transfer learning and fine-tuning, you can save significant training time and computational resources. This repository serves as a guide for individuals looking to enhance the accuracy of object detection tasks through fine-tuning, especially when working with specialized datasets.

In this project, I demonstrate the fine-tuning process using a custom vehicle detection dataset. The dataset includes images of various vehicles, and our goal is to train the Faster R-CNN model to accurately detect and classify different vehicle types. The Faster R-CNN model was pretrained on the COCO dataset and fine-tuned on a vehicle dataset from CrowdAI

Getting Started

To get started, follow the steps outlined in the Jupyter Notebook fine_tune.ipynb. This notebook provides a comprehensive walkthrough of the entire process, from dataset preparation to model training and evaluation.

Usage

  1. Clone this repository: 
    git clone https://github.com/your-username/fine-tune-object-detector.git
cd fine-tune-object-detector
  2. Install the required Python packages using the requirements.txt file: 
    pip install -r requirements.txt
  3. Open and run the Jupyter Notebook fine_tune.ipynb to start the fine-tuning process.
  4. Follow the step-by-step instructions in the notebook to fine-tune the Faster R-CNN model and evaluate its performance.

Results

The results of the fine-tuning process and the model's performance on the validation set can be visualized using the provided visualization functions. The notebook guides you through the process of interpreting the model's predictions and assessing its accuracy.