Object Detection and Recognition

Proprietary and confidential. Do not distribute.

using neon for object detection and localization

Yinyin Liu, PhD March 3 2016

Outline

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Intro to Deep Learning• From a user’s perspective, how to use neon to solve your

problem

• Use object localization problem as an example to

• understand and utilize neon architecture

• implement a new model

Object detection and localization

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Object detection and localization

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Fast region-based CNN (RCNN)

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Fast R-CNN [Girshick 2015] http://arxiv.org/abs/1504.08083

• Pre-trained the ConvNets

• ROI pooling

• Branch architecture

• cost function to consider both

functions

neon architecture

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dataset

model

Optimizer

Cost

callbacks

Layers/container

Metric

Architecture

Building fast R-CNN network

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model

Optimizer

smooth L1 cost

callbacks

loading trained VGG layers

Branch architecture

Pooling

ROI pooling

Dropout

Affine

Conv

data iterator

PASCAL VOC

Object detection metric

• Input • Image • ROIs

• Target • class label • box regression • box regression mask

new components that were not in neon

PASCAL VOC in a dataset container

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PASCAL VOC

• Input • Image • ROIs

• Target • class label • box regression • box regression mask

ROI pooling layer

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• ROI pooling layer combines feature map from a

layer, and ROIs from the dataset

• Make a ROI pooling a container

• contains the ConvNet layers

• interface with dataset directly

ROI pooling layer

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• Any new layer or container needs to:

• work as part of the model’s forward and

backward propagation process

• have fprop and bprop functions

• start from an python implementation

• write backend (GPU) support for speed

Cost and metric

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• new type of cost need to be derived from Cost

class

• new type of metric need to be derived from

Metric class

Fast R-CNN model

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Fast R-CNN model

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Trained and tested in neon

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Trained and tested in neon

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Trained and tested in neon

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Contact

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[email protected]

github.com/NervanaSystems/neon

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