This is a fork of @smallcorgi's experimental Tensorflow implementation of Faster RCNN made to run on Google Cloud Machine Learning. For details about R-CNN please refer to the paper Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.
Create a Google Cloud Project and enable Cloud ML as described here
This code should support running without a GPU / CUDA, but if compilation blows up I would not be suprised. Ideally you'll test your code locally on a machine with CUDA. In which case, be sure to set the CUDAHOME env variable, it's usually /usr/local/cuda. It's also probably a good idea to use virtualenv. Once you've activated your virtualenv, it should be as easy as:
git clone https://github.com/vanpelt/Faster-RCNN_CloudML
export CUDAHOME=/usr/local/cuda
pip install tensorflow
cd lib
python setup.py build_ext --inplaceFor training the end-to-end version of Faster R-CNN with VGG16, 3G of GPU memory is sufficient (using CUDNN)
We modified this library to support data generated by the CrowdFlower annotation tool. We asked the crowd to draw boxes around monkey's. There is a crowdflower module in the datasets directory which converts the data to imdb format. To use you'll need to copy our dataset to your bucket as described below.
We'll use the cloud-ml.sh script to submit jobs. Before we can submit jobs we have to setup our google cloud storage bucket. All jobs are run within a namespace, we'll call ours demo. The following commands upload data to the google cloud storage bucket created for our machine learning project described above.
export MY_PROJECT_NAME=funky_monkey
export NAME=demo
gsutils cp config/cfg.yml.sample gs://$MY_PROJECT_NAME-ml/$NAME/cfg.yml
gsutils cp config/class_names.json.sample gs://$MY_PROJECT_NAME-ml/$NAME/class_names.json
gsutils -m cp gs://crowdflower-machine-vision/monkey/* gs://$MY_PROJECT_NAME-ml/$NAME/
gsutils -m cp gs://crowdflower-machine-vision/VGG_imagenet.npy gs://$MY_PROJECT_NAME-ml/$NAME/Generally it's a good idea to test things locally because CloudML takes 5-10 minutes to startup. To test training locally run:
./cloud-ml -l demoIf that looks good, let's send it to the ☁️.
./cloud-ml demoIf you're feeling ambitious, you can run other datasets defined in the datasets directory. For instance, here's how to run pascal_voc
export NAME=demo_voc
gsutils cp config/voc_cfg.yml.sample gs://$MY_PROJECT_NAME-ml/$NAME/cfg.yml
gsutils -m cp gs://crowdflower-machine-vision/VOC2007 gs://$MY_PROJECT_NAME-ml/$NAME/
gsutils -m cp gs://crowdflower-machine-vision/VGG_imagenet.npy gs://$MY_PROJECT_NAME-ml/$NAME/
./cloud-ml -v demo_voc