Sunflower7788's repositories
compact_bilinear_pooling
MatConvNet and Caffe repo with compact bilinear and bilinear pooling functionality added
custom-caffe
My Caffe. I write some useful layers for experiment.
deep-residual-networks
Deep Residual Learning for Image Recognition
deeplearning
深度学习相关的模型训练、评估和预测相关代码
Deformable-ConvNets-caffe
Deformable Convolutional Networks on caffe
DenseNet-Caffe
DenseNet Caffe Models, converted from https://github.com/liuzhuang13/DenseNet
densenet.mxnet
:fire:A MXNet implementation of DenseNet (with BC structure):fire:
DenseNetCaffe
Caffe code for Densely Connected Convolutional Networks (DenseNets)
FastMaskRCNN
Mask RCNN in TensorFlow
focal-loss
Focal loss for Dense Object Detection
hed
code for Holistically-Nested Edge Detection
ICNet
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
Image_manipulation_detection
Paper: CVPR2018, Learning Rich Features for Image Manipulation Detection
jdPigRecognition
京东 JDD 大赛 猪脸识别项目
Kaggle-Carvana-3rd-Place-Solution
3rd place solution ( Carvana Image Masking Challenge )
Low-Rank-Bilinear-Pooling
Fine-grained classification via second order statistics in a compact end-to-end trainable model
Mask_RCNN
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Pig
The Pig Face Recognition Competition
Pytorch-UNet
Pytorch implementation of the U-Net for image semantic segmentation, with dense CRF post-processing
really-awesome-semantic-segmentation
A list of papers on Semantic Segmentation.
resnet-protofiles
Caffe Protofiles for MSRA ResNet: train prototxt
Triplet-Loss
Apply triplet loss to face recognition, adding new loss layer to the latest version of caffe.
VanillaCNN
Implementation of the Vanilla CNN described in the paper: Yue Wu and Tal Hassner, "Facial Landmark Detection with Tweaked Convolutional Neural Networks", arXiv preprint arXiv:1511.04031, 12 Nov. 2015. See project page for more information about this project. http://www.openu.ac.il/home/hassner/projects/tcnn_landmarks/ Written by Ishay Tubi : ishay2b [at] gmail [dot] com https://www.l