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MIT CSAIL Computer Vision (@CSAILVision)

CSAILVision

Stars
12,932
Global Org. Rank 1,762 (Top 0.6 %)
Registered over 12 years ago
Most used languages

Python
50.0 %

MATLAB
11.1 %

JavaScript
11.1 %

C++
5.6 %

Lua
5.6 %

Jupyter Notebook
5.6 %

Java
5.6 %

Objective-C
5.6 %
Location 🇺🇸 United States
Country Total Rank 790
Country Ranking

Lua
95

Python
118

MATLAB
121

Jupyter Notebook
665

JavaScript
1,348

C++
2,272

Objective-C
3,550

semantic-segmentation-pytorch

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

places365

The Places365-CNNs for Scene Classification

gandissect

Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/

LabelMeAnnotationTool

Source code for the LabelMe annotation tool.

GazeCapture

Eye Tracking for Everyone

sceneparsing

Development kit for MIT Scene Parsing Benchmark

NetDissect

Network Dissection http://netdissect.csail.mit.edu for quantifying interpretability of deep CNNs.

unifiedparsing

Codebase and pretrained models for ECCV'18 Unified Perceptual Parsing

ADE20K

Jupyter Notebook

NetDissect-Lite

Light version of Network Dissection for Quantifying Interpretability of Networks

placeschallenge

The Toolkit for Places Challenge 2017

LabelMeToolbox

MATLAB Toolbox for the LabelMe Image Database

miniplaces

MiniPlaces Challenge: an Educational Tool for Deep Learning in Computer Vision

IBD

IBD: Interpretable Basis Decomposition for Visual Explanation

LabelMeMechanicalTurk

Toolbox for interfacing LabelMe with Mechanical Turk

DetectMeApp

DetectMeMatlabToolbox

DetectMeServer