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3d-machine-learning

A resource repository for 3D machine learning

awesome-scientific-computing

:sunglasses: Curated list of awesome software for numerical analysis and scientific computing

awesome-spiking-neural-networks

Awesome Spiking Neural Networks

benchmark_results

Visual Tracking Paper List

im-loss-information-maximization-loss-for-spiking-neural-networks

Official simplified implementation of IM-Loss.

joint-a-snn

Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorization

meshsmoothing

some mesh smoothing methods

meshview

a software to generate surface triangular mesh and show triangular mesh

mpbn

Membrane Potential Batch Normalization for Spiking Neural Networks

neuroclip

open_clip

An open source implementation of CLIP.

packballs

pack balls with a size field

pcl

Point Cloud Library (PCL)

pointnet_pointnet2_pytorch

PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.

pytorch-spiking-yolov3

A minimal PyTorch implementation of Spiking-YOLOv3, based on the minimal PyTorch implementation of PyTorch-YOLOv3(eriklindernoren/PyTorch-YOLOv3), with support for spiking-yolov3-tiny at present.

pytorch_image_classification

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet

re-loss

Reducing Information Loss for Spiking Neural Networks

real-spike

Real Spike: Learning Real-valued Spikes for Spiking Neural Networks

remesh

remesher

Isotropic surface remeshing software

repdistiller

[ICLR 2020] Contrastive Representation Distillation (CRD), and benchmark of recent knowledge distillation methods

solving_imagenet

Official PyTorch implementation of the paper: "Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results" (2022)

spiking-neural-network-snn-with-pytorch-where-backpropagation-engenders-stdp

What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough.

stdp-mnist

super-gradients

Easily train or fine-tune SOTA computer vision models with one open source training library

ternary-spike

Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks

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