Please remember to put this in bin/activate after following the tensorflow model detection api’s obj:
# INstallation instruction quick summary
# For CPU
pip install tensorflow
# For GPU
pip install tensorflow-gpuThe remaining libraries can be installed on Ubuntu 16.04 using via apt-get:
sudo apt-get install protobuf-compiler python-pil python-lxml python-tk
pip install --user Cython
pip install --user contextlib2
pip install --user jupyter
pip install --user matplotlib
Alternatively, users can install dependencies using pip:
pip install --user Cython
pip install --user contextlib2
pip install --user pillow
pip install --user lxml
pip install --user jupyter
pip install --user matplotlib# FOr coco metrics
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
make
cp -r pycocotools <path_to_tensorflow>/models/research/
# From tensorflow/models/research/
protoc object_detection/protos/*.proto --python_out=.
# From tensorflow/models/research/
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
The data was prepared (made into tf-record shards of size 10) according to the guide.
The following files were edited to allow for setting up the pipeline and the hyper-parameters for faster RCNN as well as what was to be done during training.
config/faster_rcnn_resnet101_sdc.config – has the configuration. config/sdc_label_map.pbtxt – Has the class to label mapping.
utils/visualize allows one to visualize tf-record shards. Sharding was done to fit the training data onto the GPU.
the run_sdc_train.sh script is used for running a tensorflow object detection. This will have to be from the tensorflow’s model/research folder.
GCP will also have to be set up according to the installation guide.
Task 2 Instructions: Run the file Task2FinalCode.py with bbox_data in the code pointing to the correct location of the bounding box pkl file. The code generates outputTask2.csv file in the current working directory.