DeeperVision and DeepInsight Solutions

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Presentation transcript:

DeeperVision and DeepInsight Solutions Junjie Yan*, Naiyan Wang*, Yinan Yu, Linjiao Zhao, Stan Z. Li, Dit-Yan Yeung * denotes equal contribution

DeeperVision Classification Deeper network always helps

DeeperVision Classification Nesterov method based optimization With large momentum and Nesterov based optimization method, the algorithm could smooth out the optimization path. It can improve top 1 accuracy by 0.8%

DeeperVision Classification More findings… Slow down the speed of data abstraction (stride, kernel size, etc.) More complicated data augmentations Spatial Pyramid Pooling (SPP) Our final results Single net: Top 5 error: 10.5% Ensemble 5 nets: Top 5 error: 9.5%

Deep Insight Detection Region proposal + CNN feature extraction Selective Search + Structural Edge [1] for region proposal. 7/8/9 Convolution Layers + SPM +2 Fully Connected Layers. Deeper Models need more tuning iterations. Better (Deeper) Classification CNN always helps Detection. [1]C. Lawrence Zitnick and Piotr Dollár Edge Boxes: Locating Object Proposals from Edges ECCV 2014

Diagnosis Experiments (on 2013-val2 ) Original RCNN 31.4 + 9conv + SPM 36.6 + more iterations 39.2 + Structural Edge Proposal 40.1 + 7/8/9 Conv Ensemble 40.7 + CLS Context 42.0

Our Final Result We have the best single model (40.2 mAP V.S. the 38.0 mAP of GoogLeNet) We use a non-optimal ensemble method when submitting result. A better ensemble method leads to a 42.0 mAP on val2 after the competition. Keeps improving…

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