This repository is a base docker container for Tsinghua-Tencent 100K object detection docker container.
It is tested with Nvidia Geforce GTX 1080 Ti.

• Docker CE 18.06.2-ce
• Nvidia Docker2
• Host O/S Nvidia Driver 418.87.00
• Ubuntu 16.04
• CUDA 8.0
• cuDNN 6.0
• Python 2.7.12
• Caffe of TT100K

• Docker repository and tag name is "aibakevision/object-detector-tt100k-base-gpu:cuda8.0-ubuntu16.04-python2.7.12-gh".

./build.sh

• Push docker image

./push.sh

• Run docker image
• For volumn binding, it needs host path parameter and binding path parameter in Docker container.

./run.sh ~/data_folder /workspace/data_folder

• On Docker Bash shell, prepare tt100k dataset.

cd data_folder && wget http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/data.zip && unzip data.zip && mkdir -p /data/lmdb
cd ../tsinghua-tencent-100k/code/script && ./prepare.sh

• On Docker Bash shell, train the tt100k dataset
• Before training, you need to set the dataset path in train_val.prototxt file. (/workspae/data_folder/data)

cd code/script && ./train.sh

It is cited from:

@InProceedings{Zhe_2016_CVPR,
author = {Zhu, Zhe and Liang, Dun and Zhang, Songhai and Huang, Xiaolei and Li, Baoli and Hu, Shimin},
title = {Traffic-Sign Detection and Classification in the Wild},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2016}
}