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chaotaklon edited this page Jun 27, 2017 · 47 revisions

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Awesome deep vision:

Caffe model zoo:

  • Berkeley-trained models
  • Network in Network model
  • Models from the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional Nets"
  • Models used by the VGG team in ILSVRC-2014
  • Places-CNN model from MIT.
  • GoogLeNet GPU implementation from Princeton.
  • Fully Convolutional Networks for Semantic Segmentation (FCNs)
  • CaffeNet fine-tuned for Oxford flowers dataset
  • CNN Models for Salient Object Subitizing.
  • Deep Learning of Binary Hash Codes for Fast Image Retrieval
  • Places_CNDS_models on Scene Recognition
  • Models for Age and Gender Classification.
  • GoogLeNet_cars on car model classification
  • ParseNet: Looking wider to see better
  • SegNet and Bayesian SegNet
  • Conditional Random Fields as Recurrent Neural Networks
  • Holistically-Nested Edge Detection
  • CCNN: Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
  • Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
  • Facial Landmark Detection with Tweaked Convolutional Neural Networks
  • Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
  • ResNets: Deep Residual Networks from MSRA at ImageNet and COCO 2015
  • Pascal VOC 2012 Multilabel Classification Model
  • SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
  • Mixture DCNN
  • CNN Object Proposal Models for Salient Object Detection
  • Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled
  • Mulimodal Compact Bilinear Pooling for VQA
  • Pose-Aware CNN Models (PAMs) for Face Recognition
  • Learning Structured Sparsity in Deep Neural Networks
  • Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
  • Inception-BN full ImageNet model
  • ResFace101: ResNet-101 for Face Recognition
  • DeepYeast
  • ImageNet pre-trained models with batch normalization
  • ResNet-101 for regressing 3D morphable face models (3DMM) from single images
  • Cascaded Fully Convolutional Networks for Biomedical Image Segmentation
  • Deep Networks for Earth Observation
  • Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks
  • Striving for Simplicity: The All Convolutional Net

MatConvNet pretrain:

  • Object detection Fast R-CNN
  • Face recognition VGG-Face
  • Semantic segmentation Fully-Convolutional Networks, BVLC FCN, Torr Vision Group FCN-8s
  • ImageNet ILSVRC classification ResNet, GoogLeNet, VGG-VD, VGG-S,M,F, Caffe reference model, AlexNet

Hong Kong Deep Learning:

CUHK IE Media lab:

Others:

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