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acquisition2p_class

For managing 2P imaging datasets from preprocessing to activity trace extraction

b-soid

Behavioral segmentation of open field in DeepLabCut, or B-SOID ("B-side"), is a pipeline that pairs unsupervised pattern recognition with supervised classification to achieve fast predictions of behaviors that are not predefined by users.

biggan-pytorch

The author's officially unofficial PyTorch BigGAN implementation.

cortexmodel

Recurrent cortical circuit model based on adaptive-exponential integrate and fire neurons. Includes 3 interneuron subtypes (PV, SOM, and VIP). Implemented in MATLAB (2016b)

deepethogram

deeplabcut

Markerless pose estimation of user-defined features with deep learning for all animals including humans

deeplabcut-core

Headless DeepLabCut (no GUI support)

jbohnslav.github.io

kornia

Open Source Differentiable Computer Vision Library for PyTorch

lightning

Build and train PyTorch models and connect them to the ML lifecycle using Lightning App templates, without handling DIY infrastructure, cost management, scaling, and other headaches.

logistic_regression

mnist_viz

Simple visualizations of neural activation during mnist classification

neural_networks_intro

Harvard PiN Nerd Club's introduction to neural networks.

neural_renderer

A PyTorch port of the Neural 3D Mesh Renderer

opencv_transforms

OpenCV implementation of Torchvision's image augmentations

pimaq

Multiple camera acquisition toolbox in Python

pose_annotator

Free, open-source GUI for annotating keypoints for pose estimation

pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

pytorch-ssim

pytorch structural similarity (SSIM) loss

timelapse

tinyimagenet_benchmarking

Timing script for one epoch of TinyImageNet. Can be used to evaluate performance of deep learning workstations or cloud machines.

video_cropper

vidio

simple, performant video reading and writing in python

vllm

A high-throughput and memory-efficient inference and serving engine for LLMs