Code structure is re-organized, which is easier to read and modify data_manager is split into different scripts. Added --fixbase option, allowing randomly initialized classifier layer to be trained while keeping base network frozen. Rewrote sampler for triplet loss, which allows most of the training images to be covered during a training epoch. Added requirements.txt, which eases environment configuration. Instructions on data preparation and benchmarks are separated into different files. To be done: --lmdb is under development
© 1979-2012 IEEE. Recent years have witnessed the success of deep neural networks in dealing with a ...
During minibatch gradient-based optimization, the contribution of observations to the updating of th...
Person ReID is the problem of matching people across many different camera views, also known as mult...
multi-GPU training. both image-based and video-based reid. unified interface for different reid mode...
Added --load-weights (weights that don't match in size will be discarded, e.g. old classification la...
Major updates: Model codes such as resnet.py and densenet.py keep the original style for easier mod...
In person re-identification (ReID) task, because of its shortage of trainable dataset, it is common ...
Main update: Plot ranks in a single figure (currently support image-reid) Visualize activation maps...
Major updates: Significant changes to "scripts/main.py", where most arguments in argparse are repla...
Person re-identification (ReID) focuses on identifying people across different scenes in video surve...
DeepJetCore: Package for training and evaluation of deep neural networks for HEP This package provi...
A novel technique for deep learning of image classifiers is presented. The learned CNN models higher...
Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s input...
This data contains raw PDB files and other metadata that can be used to run DeepRank-Core tutorials ...
Deep neural networks (DNNs) require large amounts of labeled data for model training. However, label...
© 1979-2012 IEEE. Recent years have witnessed the success of deep neural networks in dealing with a ...
During minibatch gradient-based optimization, the contribution of observations to the updating of th...
Person ReID is the problem of matching people across many different camera views, also known as mult...
multi-GPU training. both image-based and video-based reid. unified interface for different reid mode...
Added --load-weights (weights that don't match in size will be discarded, e.g. old classification la...
Major updates: Model codes such as resnet.py and densenet.py keep the original style for easier mod...
In person re-identification (ReID) task, because of its shortage of trainable dataset, it is common ...
Main update: Plot ranks in a single figure (currently support image-reid) Visualize activation maps...
Major updates: Significant changes to "scripts/main.py", where most arguments in argparse are repla...
Person re-identification (ReID) focuses on identifying people across different scenes in video surve...
DeepJetCore: Package for training and evaluation of deep neural networks for HEP This package provi...
A novel technique for deep learning of image classifiers is presented. The learned CNN models higher...
Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s input...
This data contains raw PDB files and other metadata that can be used to run DeepRank-Core tutorials ...
Deep neural networks (DNNs) require large amounts of labeled data for model training. However, label...
© 1979-2012 IEEE. Recent years have witnessed the success of deep neural networks in dealing with a ...
During minibatch gradient-based optimization, the contribution of observations to the updating of th...
Person ReID is the problem of matching people across many different camera views, also known as mult...