Jim Bohnslav's repositories

opencv_transforms

OpenCV implementation of Torchvision's image augmentations

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vidio

simple, performant video reading and writing in python

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PIMAQ

Multiple camera acquisition toolbox in Python

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pose_annotator

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

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Acquisition2P_class

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

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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.

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BigGAN-PyTorch

The author's officially unofficial PyTorch BigGAN implementation.

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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)

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DeepLabCut

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

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DeepLabCut-core

Headless DeepLabCut (no GUI support)

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kornia

Open Source Differentiable Computer Vision Library for PyTorch

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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.

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mnist_viz

Simple visualizations of neural activation during mnist classification

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neural_networks_intro

Harvard PiN Nerd Club's introduction to neural networks.

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neural_renderer

A PyTorch port of the Neural 3D Mesh Renderer

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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

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pytorch-ssim

pytorch structural similarity (SSIM) loss

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tinyimagenet_benchmarking

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

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vllm

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

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