README.md

* Lyft Perception Challenge *

The lyft Perception challenge in association with Udacity had an image segmentation task where the candidates had to submit their algorithm which could segment road and cars pixels as precisely as possible. The challenge started on May 1st,2018 and went through June 3rd, 2018.

Approach

Although it was a segmentation problem and did not require instance segmentation, I went ahead with MASK-RCNN as it was the state of the art algorithm in image segmentation and I was always intrigued to learn about it.

Mask-RCNN

Mask-RCNN, also known as Detectron is a research platform for object detection developed by facebookresearch. It uses Resnet as the backbone, For this application Resnet-50 was used by setting BACKBONE = "resnet50" in config.

Training

heads Epoch	all Epoch	loss	val_loss
10	40
40	100
10	40
20	60

Results

Your program runs at 1.703 FPS

Car F score: 0.519 | Car Precision: 0.509 | Car Recall: 0.521 | Road F score: 0.961 | Road Precision: 0.970 | Road Recall: 0.926 | Averaged F score: 0.740

Inference and Submission

Submission

Submission requires files to be encoded in a json. test_inference.py contains the inference and submission code. In attempt to increase the FPS, The encode function was replaced with the follows which was shared on the forum

def encode(array):
    retval, buffer = cv2.imencode('.png', array)
    return base64.b64encode(buffer).decode("utf-8")

Reference

https://github.com/matterport/Mask_RCNN

@misc{Charles2013,
  author = {waleedka et.al},
  title = {Mask R-CNN for Object Detection and Segmentation},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/matterport/Mask_RCNN}},
  commit = {6c9c82f5feaf5d729a72c67f33e139c4bc71399b}
}

Author

Ameya Wagh aywagh@wpi.edu