The MNIST Dataset

In this example, we used the MNIST dataset (handwritten digits) to train a convolutional neural network (CNN) for image classification, and reached a validation accuracy of ~0.98 on our trained model.

In this example, we used the MNIST dataset (handwritten digits) to train a convolutional neural network (CNN) for image classification, and reached a validation accuracy of ~0.98 on our trained model.

Domain Gap

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.

Cifar10 – Resnet

This project implements the resnet algorithm using CIFAR-10 dataset for image classification tasks.

CityScapes – Object Detection

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.

Optical Flow – Raft

This project utilizes the RAFT (Recurrent All-pairs Field Transforms) model that estimates Optical Flow using the KITTI dataset.