Fork of weigao95/mrcnn_integrate

mrcnn_integrate

Contents of upstream README.md below.

Notes:

Installation and Dependencies

This project uses the maskrcnn-benchmark project developed by facebook research which is not maintained anymore. Here a fork of maskrcnn-benchmark which updates some things to keep this repository usable.

To run this project, use the Dockerfile in the linked maskrcnn-benchmark fork. Use it to train the network and make predictions. An embarrassingly simple server is scripted in inference/prediction_server.py. Send images to this server running in the docker container and receive predictions.

Using a Pretrained Model

A script was added to the train_tools folder which helps with using existing models. Once you downloaded a pretrained model, this script helps with changing the model's architecture to make it suitable for the number of classes you want it to predict.

Create Dataset

Will convert the dataset structure from the one used with pytorch-dense-correspondence to coco data set format.

# add this module to the pythonpath
export PYTHONPATH=`pwd`":${PYTHONPATH}"

cd dataproc/scripts

# builds the dataset. See config file to adjust dataset generation.
python3 build_dataset.py

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Upstream README.md

This repo is part of kPAM that provides the data generator and training script for the maskrcnn-benchmark. This repo don't need the ros runtime.

To use this repo. please first follow the instruction here to setup the dataset. After setup, you can run mrcnn_integrate/dataproc/scripts/build_dataset.py to generate a coco dataset for maskrcnn training. You need to change the config in the build_singleobj_database function, which is the same as the consruct_datset function described here.