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3rdparty_diffusionmodelsfromscratch

Creating a diffusion model from scratch in PyTorch to learn exactly how they work.

3rdparty_learnit

aambotu

activitywatch

The best free and open-source automated time tracker. Cross-platform, extensible, privacy-focused.

awesome-dataset-tools

🔧 A curated list of awesome dataset tools

awesome_trailergeneration

This repository lists important papers on automated trailer generation.

bagoftricks-lt

A scientific and useful toolbox, which contains practical and effective long-tail related tricks with extensive experimental results

bigcode-dataset

c-rbp

cityscapes-api

API for Cityscapes Dataset

cityscapesscripts

README and scripts for the Cityscapes Dataset

clearml_sarcasm_detector

This is a tutorial from clearml and is useful as a handson onboarding experiment.

clevr-dataset-gen

A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

cudaimagewarp

Quickly warp 3D images on the GPU using CUDA. Works with C and Python.

datasharing

The Leek group guide to data sharing

deepco3

[CVPR19] DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency (Oral paper)

depthimage_to_pointcloud2

ROS2 package to convert a depth image to a pointcloud2 (and color from a RGB image)

design-practice-repository

Summaries of artifacts, templates, practices, and techniques for agile architecting (DPR-mm) and service design (SDPR-nn).

diffeomorph

documentary-learning

Code for the paper "Discover and Learn New Objects from Documentaries", CVPR2017

eidolon

Eidolons: Novel Stimuli for Vision Research

eyemu

Gaze + IMU Gestures on Mobile Devices

featurelearningrotnet

fondant

Large-scale data processing made easy and reusable

fully-local-pdf-chatbot

functa

g-vue

General-purpose Visual Understanding Evaluation

git-cheat-sheet

A cheat sheet for Git workflows.

grounded-language-learning-in-pytorch

Implementation of Grounded Language Learning in a 3D Simulated World (DeepMind)

higher

higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.