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Centre of Excellence in Machine Learning
Project 1: Car model analysis with artificial neural networks
Objectives
Explore possible neural architectures to handle the problem of car model identification using grained features.
Reproduce the results reported in the paper linked
here
.
Practical applications
Paying tolls.
Video surveillance and car verification.
Smart cities.
Mobile applications to instantly show car information.
Predicting popularity of vehicles.
Datasets
The Comprehensive Cars (CompCars) dataset
Stanford dataset
Common Objects in Context (COCO) dataset
The CIFAR-10 dataset
Tools/Frameworks
Caffe
.
Keras
with
TensorFlow
backend.
Pre-trained tensorflow models
Tensorflow for poets
Expected Results
Deliver a trained neural network for car model identification
Outperform accuracy scores reported by the
chinese paper
.
Checklist
Request
CompCars
database.
Define development machine and database storage.
Explore the data and gain insights.
Evaluate and define preprocessing steps.
Implementation of the neural network (NN) models.
Deep convolutional neural network: Inception
.
Pulsed-coupled NN
.
Presentation of results in any relevant conference (to define...)
References
Speed/accuracy trade-offs for modern convolutional object detectors
View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network
.
Using Convolutional Neural Networks to distinguish vehicle pose and vehicle class
.
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
.
Three-Dimensional Deformable-Model-Based Localization and Recognition of Road Vehicles
.
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Topic revision: r7 - 2018-02-06
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jruizvar
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