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mofanerf-original

MoFaNeRF: Morphable Facial Neural Radiance Field

nerf-pytorch

A PyTorch re-implementation of Neural Radiance Fields

nerf-pytorch-1

A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

nerf_pl

NeRF (Neural Radiance Fields) and NeRF in the Wild using pytorch-lightning

nord-iterm2

An arctic, north-bluish clean and elegant iTerm2 color scheme.

p0

pixel-deflection

Deflecting Adversarial Attacks with Pixel Deflection

question-relevance-in-vqa

Project for 10-805: Machine Learning for large datasets

recommendation-system-with-million-song-dataset

10605 group 8 spring 2020

reddit-score-prediction

Final Project for 10-605: ML with Large Datasets

robot-localization-and-mapping

CMU 16833 Homework and projects

salsa-semantic-assisted-slam

SALSA: Semantic Assisted Life-Long SLAM for Indoor Environments (16-833 SLAM Project at CMU)

segmentation-and-detection-of-work-zone-scenes

This project is concerned with the automatic detection and analysis of work zones (construction zones) in naturalistic roadway images. An underlying motivation is to identify locations that may pose challenges to advanced driver-assistance systems or autonomous vehicle navigation systems. We first present an in-depth characterization of work zone scenes from a custom dataset collected from more than a million miles of naturalistic driving data. Then we describe two ML algorithms based on the ResNet and U-Net architectures. The first approach works in an image classification framework that classifies an image as a work zone scene or non-work zone scene. The second algorithm was developed to identify individual components representing evidence of a work zone (signs, barriers, machines, etc.). These systems achieved an F{0.5} score of 0.951 for the classification task and an F1 score of 0.611 for the segmentation task. We further demonstrate the viability of our proposed models through salience map analysis and ablation studies. To our knowledge, this is the first study to consider the detection of work zones in large-scale naturalistic data. The systems demonstrate potential for real-time detection of construction zones using forward-looking cameras mounted on automobiles.

sim

sim-project-page

terminal-settings

terraform-aws-spotgpu

Fully automated provisioning of AWS EC2 Spot Instances for Deep Learning workloads using Terraform.

timemap-mobile

timemap-web

unsupervised

Homework for Deep Unsupervised Learning (CS294-158) course

visual-learning

Assignments for CMU 16-824 Visual Learning and Recognition

visual-learning-recognition-16824

vqa_pytorch

VQA assignment for course 16824

wsddn.pytorch

Implementation of Weakly Supervised Deep Detection Networks using the latest version of PyTorch