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caffe-ccnn

Branch of caffe for weakly supervised learning.

caffe-dense-img-seg

caffe per pixel segmentation

caffe-fast-rcnn

Caffe fork that supports Fast R-CNN

caffe-python-layers

Python Layers for Caffe.

caffe_rtpose

Realtime C++ code for multi-person pose estimation

caffe_tools

Matlab wrapper functionality for recent Caffe layout

calibration

Supplemental material for "Simultaneous calibration of odometry and sensor parameters for mobile robots"

caltech_youbot

Information and code for the KUKA YouBot at Caltech/CDS Robot Lab

cam_imu_sync

Synchronization driver for Camera and IMU

cam_imu_sync-1

camera_base

Some base classes for simplifing ROS camera driver node.

captcha_recognition

car_demo

cartographer

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

catkin_simple

catkin, simpler

ccnn

[ICCV 2015] Framework for optimizing CNNs with linear constraints for Semantic Segmentation

cg_mrslam

A ROS package that implements a multi-robot SLAM system using the condensed graphs approach

char-rnn-tensorflow

Multi-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow

chatbot-retrieval

Dual LSTM Encoder for Dialog Response Generation

cheat-sheet

C++ Cheat Sheet for ACM ICPC

cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers

cherry-autonomous-racecar

Implementation of the CNN from End to End Learning for Self-Driving Cars on a Nvidia Jetson TX1 using Tensorflow and ROS

chrono

C++ library for multi-physics simulation

cis519project

cis520_autoboost

Winning submission for the CIS520 Machine Learning final competition 2013

cis650_projects

cis680_2017fall

This is the repo for releasing code related to CIS 680 in 2017 Fall.

cmcode

Ming-Ming Cheng's open source projects

cmvs-pmvs

This software (CMVS) takes the output of a structure-from-motion (SfM) software as input, then decomposes the input images into a set of image clusters of managable size. An MVS software can be used to process each cluster independently and in parallel, where the union of reconstructions from all the clusters should not miss any details that can be otherwise obtained from the whole image set. CMVS should be used in conjunction with an SfM software Bundler and an MVS software PMVS2 (PMVS version 2).

cn24

Convolutional (Patch) Networks for Semantic Segmentation