drivable-area-segmentation

This project implements driving area segmentation by applying on transfer learning on Mask R-CNN model here. This work was done as a part of CS230 class at Stanford, winter 2019. Project report can be found here on GitHub and is also posted on class past projects page as of on the day of writing this update. We have used Berkeley Driving Dataset, for rest of the project acronym BDD has been used.

The repository includes:

inspect_bdd_data.ipynb visualize the training data and masks
inspect_bdd_model.ipynb visualizes the detection pipeline at every step
inspect_bdd_results.ipynb analyzes segmentation results on the validation dataset.
shrink.ipynb creates label files from the master label file corresponding to images in a folder.
bdd.py Training code for BDD

Getting started

In order to run transfer learning on Mask R-CNN

Clone matterport Mask-RCNN repo

git clone https://github.com/matterport/Mask_RCNN

Clone this repository

cd /Mask_RCNN/samples/
mkdir bdd
git clone https://github.com/beeRitu/drivable-area-segmentation

Ensure all the jupyter notebooks are in the folder

/Mask_RCNN/samples/bdd

Download the dataset

Dataset can be downloaded from Berkeley Driving Dataset. There are 70k training images, 10k validation images and 20k test images. These should be copied in the following path

/Mask_RCNN/datasets/bdd/train #70,000 training images
/Mask_RCNN/datasets/bdd/val #10,000 training images
/Mask_RCNN/datasets/bdd/test #20,000 training images

In addition, copy the 2 label files in the path indicated below

/Mask_RCNN/datasets/bdd/bdd100k_labels_images_train.json #labels for all 70,000 training images
/Mask_RCNN/datasets/bdd/bdd100k_labels_images_val.json #labels for all 10,000 validation images

Visualize the training data

You can create a small subset of the training data for the initial visualization. To do this, rename the folder train containging 70k training images to something like train70k and copy a subset of training images to the folder train. 1000 images might be a good starting point. Run shrink.ipynb to create a shortened version of the label file corresponding to the number of images in train. This will create file named via_region_data.json in folder train. Check the following assignments before running the notebook

master_json="bdd100k_labels_images_train.json" # label file for 70k images in /Mask_RCNN/datasets/bdd/
dataset_dir=os.path.abspath("../../datasets/bdd/train") # image folder where json file will be created
output_json="via_region_data" # Name of the output json file

Training the model

We started the training from pretrained MS COCO models. Training and evaluation code is in samples/bdd/bdd.py. Training can be started directly from command line as such:

# Train a new model on bdd dataset starting from pre-trained COCO weights
python3 samples/bdd/bdd.py train --dataset=/datasets/bdd/ --weights='coco'

# Continue training a model that you had trained earlier
python3 samples/bdd/bdd.py train --dataset=/datasets/bdd/ --weights=logs/<weights directory>/<weights.h5 file to be used>

The training schedule, learning rate, and other parameters should be set in samples/bdd/bdd.py.

Visualizing the model

Model can be visualized by running the notebook inspect_bdd_model.ipynb. It generates results for the intermediate and final steps in the model.

Results and Analysis

Results can be analyzed by running the notebook inspect_bdd_results.ipynb