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.

Detecting Pneumonia – Chest X-Ray Images

The project uses transfer learning from a pre-trained DensNet201 network to classify chest X-ray images into one of three classes- normal, bacteria, or virus. In a DenseNet, we use dense connections between layers, using Dense Blocks, where each layer (with identical feature maps) directly connects with each other through dense connections. On the left panel of the chest X-ray […]

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.

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.