LICEcap simple animated screen capture tool for Windows and OS X

Python package for streaming video from multiple cameras to disk. Features real-time compression and debayering using FFmpeg.

A deep learning framework for multi-animal pose tracking.

Scalabel: A versatile web-based visual data annotation tool

a napari plugin for labeling and refining keypoint data within DeepLabCut projects

a wavelet transform for mapping behavior

Analysis of clock neurons in Hemibrain data

GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

Analysis software for voltage imaging

Tensorflow 2.3.0 implementation of DeepLabV3-Plus

🐍 Example Python project using best practices 🥇

Particle Video Revisited

📼 Tool for extracting scenes with motion from videos (e.g. security camera or DVR footage). Written in Python, uses OpenCV.

Behavioral Keypoint Discovery

Bayesian learning and inference for state space models

Toolbox for classification of animal behavior in video

Code repository of the paper T-LEAP: occlusion-robust pose estimation of walking cows using temporal information

Aydin — User-friendly, Fast, Self-Supervised Image Denoising for All.

FFmpeg for browser, powered by WebAssembly

This is the python client for accessing REST APIs within the Connectome Annotation Versioning Engine.

An active learning platform for expert-guided, data efficient discovery of behavior.

Deep Learning for Time Series Classification

High level pandas-based API for batch analysis of Calcium Imaging data using CaImAn

PyGWalker: Turn your dataframe into an interactive UI for visual analysis

Generates neuroscience videos from high-level descriptions using Blender or VVDViewer.

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Move and resize windows on macOS with keyboard shortcuts and snap areas

A tool for efficient semi-supervised video object segmentation (great results with minimal manual labor) and a dataset for benchmarking

Applied generative adversarial networks (GANs) to do anomaly detection for time series data