ReSORT-IT

Getting Started

see the INSTALLATION.txt file for details

Requirements

To install the required python packages simply type

pip install -r requirements.txt

Built With

• Mask_RCNN for object detection and instance segmentation on Keras and TensorFlow. W. Abdulla, https://github.com/matterport/Mask_RCNN, 2017, maskR-CNN for object detection and instance segmentation on Keras andTensorFlow.

Download the Datasets

To download the datasets with images and annotation files from:

Basic Dataset https://drive.google.com/file/d/1UVJ3XHo0a7VB648uNIAIMqWt99R7Yhcg/view?usp=sharing

Synthetic-Single Dataset https://drive.google.com/file/d/16g5A-Hxh4qNATRWb0c1UdiBecLhfmIxN/view?usp=sharing

Synthetic-Complex Dataset https://drive.google.com/file/d/18EeXpoLuDhgnUoVXEiw4lhYWZxrTmZRa/view?usp=sharing

Running the tests

Running the source codes

Publications

Vision-based Material Classification for Robotic Urban Waste Sorting

Authors

M. Koskinopoulou, F. Raptopoulos, G. Papadopoulos, N. Mavrakis and M. Maniadakis See the list of contributors who participated in this project.

License

This project is licensed under the BSD License - see the LICENSE.md file for details

Acknowledgments

• This research has been co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project name: ANASA, code:T1EDK-03110), MIS 5031867.

Results

Setting up tf environment

https://mc.ai/machine-learning-deep-learning-toolkit-installation-on-windows-10/

conda install -c conda-forge opencv

https://github.com/matterport/Mask_RCNN pip install -r requirements.txt python setup.py install

conda install shapely

python -m pip install tensorflow-gpu==1.13.1

import tensorflow as tf

print(tf.version) ---> 1.13.1

meta to pip install 1.13.1 conda install -c anaconda tensorflow-gpu

import keras

print(keras.version) ---> pip install keras==2.2.5

conda install git pip install Cython pip install "git+https://github.com/philferriere/cocoapi.git#egg=pycocotools&subdirectory=PythonAPI"

Plot Losses using Tensorboard

tensorboard --logdir=F:\home\microralp\mk_dev\ReSort-IT\mask_rcnn\logs