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.