Project Title: Synthetic Image Data Generator Using GANs for Computer Vision Research
Description: This project involved the development of a synthetic image data generator using Generative Adversarial Networks (GANs). My primary goal was to contribute to research in computer vision, object detection, and machine learning. The generator was designed to produce synthetic images that closely mimic the data it was originally trained on, providing researchers with an expanded dataset for training machine learning models, particularly in scenarios where acquiring large labeled datasets is challenging or expensive.
By employing GANs, the project created a set of synthetic images that were used to enhance training datasets for various computer vision tasks. This augmentation enabled improvements in model accuracy and object detection performance. Particularly for use in environments such as remote sensing.
The project showcases the potential of GAN-based synthetic data generation in overcoming data scarcity and improving the learning of machine learning models.
Technologies Used:
Python TensorFlow and PyTorch (for building and training the GAN) OpenCV and NumPy (for image processing) Impact: The project contributed to ongoing research by generating 100,000+ synthetic images, enabling more comprehensive and efficient model training, and offering a valuable tool for researchers in computer vision and machine learning fields.