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.
Enhancing ADAS Object Detection with YOLOv7: Overcoming Data Shifts through Latent Space Analysis and Efficient Data Synthesis
Tensorleap enables efficient exploration of a model’s latent space to detect data shifts in unseen samples, including unlabeled data. This repository showcases how to detect and address such issues, specifically within an object detection task for ADAS.
GLPN: Monocular Depth Estimation
Depth estimation task using a single image to predict depth.
CelebA Dataset Gender and Age Group Prediction with MobileNetV2
This project showcases a deep learning classification task for predicting the gender and age group of face individuals in images. We train a MobileNetV2 model to perform this task on CelebA benchmark using Keras and Tensorflow implementation.
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 […]
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.
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.