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3) Enlarge the msra-net to 25 layers, and change some BN-net input from 224x224 to 270x270.
We subsample 15K object categories from the 22K ImageNet dataset, for which more than 200 training examples are available.It is worth noting that we set most of parameters empirically because we have no time to validate them.2) Using Randomized ReLU units.Before we train models using the Fast-rcnn framework, we retrain VGG-162 with object-level annotations from CLS/LOC and DET data as with.Learn to rank(hypothesisthe data number distribution between validation set and test set is similar.Ouyang etal, "DeepID-Net: Deformable Deep Convolutional Neural Networks for Object detection cvpr 2015.The training of Henry machine for Scene401 was also done using the home-brewed CPU cluster, and took 21 days to complete (not counting algorithm design/development/debugging time).Then a Multi-Object Tracking (MOT) 6 method is utilized to associate the detections for each snippet.Detectors are based on the combination of two types of models,.e.Lenc, arXiv:1412.4564, 2014 2 "Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan and Andrew Zisserman, arXiv technical report, 2014 3 roulette chat deutsch "Return of the Devil in the Details: Delving Deep into Convolutional Networks Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman, bmvc 2014.After further exploiting the co-occurrences relationship between categories, we can determine the most possible categories for an image in advance.4) Test-time data augmentation: We applied 30 different random data augmentations to 30 copies of each test image, passed them through net and averaged predictions.ImageNet Classification with Deep Convolutional Neural NetworksJ.Rich Feature Hierarchies 2018 draft lottery for Accurate Object Detection and Semantic SegmentationC/ Computer Vision and Pattern Recognition (cvpr 2014 ieee Conference.
It is worth noting that the last model needs about 6g GPU memory to train and.5g GPU memory to test.