This project addresses a domain gap between KITTI and Cityscapes datasets for semantic segmentation in autonomous driving scenarios. By applying domain adaptation techniques and designing a robust model, we aim to improve cross-dataset generalization. The project seeks to enhance the model’s real-world applicability and contribute to advancements in domain adaptation research.
This project implements the Albert algorithm using the SQuAD (Stanford Question Answering Dataset) for question answering tasks.
This project implements the resnet algorithm using CIFAR-10 dataset for image classification tasks.
This project utilizes the YOLOv7 model to perform object detection task on Cityscapes dataset. The cityscapes dataset is a large-scale dataset that stands as one of the standard advanced driver-assistance system (ADAS) benchmarks for multiple vision-related tasks.
This project utilizes the RAFT (Recurrent All-pairs Field Transforms) model that estimates Optical Flow using the KITTI dataset.