davidbrai / Deep Learning Traffic Lights

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.