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davidbrai
461 Stars 168 Forks BSD 2-Clause "Simplified" License 4 Commits 4 Opened issues

Description

Code and files of the deep learning model used to win the Nexar Traffic Light Recognition challenge

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Recognizing Traffic Lights with Deep Learning

This repo contains the files used to train and run the classifier described in this blog post. This was done during a challenge by Nexar to recognize traffic lights based on images taken by their dashcam app.

Dependencies

Caffe with python bindings.

Directory contents:

/model
: contain a caffe deploy.prototxt file and three weights files. The three weights files are used together in a model ensemble.

/testing
: has jupyter notebook files that run the model and perform the weighted average.

/training
: contains the files needed to train the model (except the training data)

Training the model

The images were first converted to lmdb format and resized to 256x256 using this command:

GLOG_logtostderr=1 ~/caffe/build/tools/convert_imageset \
    --resize_height=256 --resize_width=256 --shuffle  \
    ~/nexar/images/ \
    ~/nexar/labels_test.txt \
    ~/nexar/lmdb/test_lmdb

Each model has a directory in

training
with some or all of the following files:
solver.prototxt   caffe solver file
solver_p2.prototxt  caffe solver file with lower base learning rate
train_val.prototxt  network training file
rotation_layer.py   python caffe layer for data augmentation with rotation

squeeze_net_manual_scratch__os
was training from scratch. The other two models were fine-tuning from weights trained on ImageNet. The weights file is named
squeezenet_v1.0.caffemodel
.

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