pchank Goto Github PK

caffe-segnet

Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling

caffe-sl

caffe for Similarity Learning

caffe-yolo

YOLO (Real-Time Object Detection) in caffe

caffe_3d_faster_rcnn

Caffe for 3D organ localization in CT image

ccpnet

3D Semantic Scene Completion

cf2

Hierarchical Convolutional Features for Visual Tracking (ICCV 2015)

cfcnet

This is the project of the paper "Conditional Feature Coupling Network for Multi-Person Clothing Parsing" (PCM2018). The test code is coming soon in Pytorch

cflml

Multiple closed-form local metric learning for KNN

cfnn

CFNN: Correlation Filter Neutral Network for Visual Object Tracking

cgnet

CGNet: A Light-weight Context Guided Network for Semantic Segmentation

channel-pruning

Channel Pruning for Accelerating Very Deep Neural Networks

cityscapesscripts

README and scripts for the Cityscapes Dataset

cn24

Convolutional (Patch) Networks for Semantic Segmentation

cnn-benchmarks

Benchmarks for popular CNN models

cnn-levelset

Source code for Kristiadi and Pranowo, 2017's "Deep Convolutional Level Set Method for Image Segmentation"

cnn-models

cocostuff

The official homepage of the COCO-Stuff 10K dataset.

coda

Implementation for CODA

colearn

compact_bilinear_pooling

MatConvNet and Caffe repo with compact bilinear and bilinear pooling functionality added

competitivesenet

Source code of paper: Competitive Inner-Imaging Squeeze and Excitation for Residual Network

continuouscrf-cnn

copanet

accepted by ACML2017

covid-ct

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

cpd

Code of Cascaded Partial Decoder for Fast and Accurate Salient Object Detection (CVPR2019)

crelu

CReLU (Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units) with Caffe

crfasrnn

This repository contains the source code for the semantic image segmentation method described in the ICCV 2015 paper: Conditional Random Fields as Recurrent Neural Networks. http://crfasrnn.torr.vision/

crowd-counting-scnn

This project is an implementation of the crowd counting model proposed in our CVPR 2017 paper - Switching Convolutional Neural Network(SCNN) for Crowd Counting. SCNN is an adaptation of the fully-convolutional neural network and uses an expert CNN that chooses the best crowd density CNN regressor for parts of the scene from a bag of regressors. This helps it tackle intra-scene crowd density variation and obtain SOTA results

crowdcount-mcnn

Single Image Crowd Counting via MCNN (Unofficial Implementation)

cspn