ID: 5d89deb0a0171346e315e53a

Masked RCNN

by SHIVAM GARG

Image Segmentation Using Masked RCNN


License: MIT license

Tags: Masked RCNN Image Segementation Keras Tensorflow Computer Vision

Ā Model stats and performance
Dataset Used MS COCO
Framework Keras
OS Used Linux
Publication View
Inference time in seconds per sample.

Screenshots


Masked RCNN for Image segmentation

WHAT IS IT?

Mask R-CNN is an instance segmentation technique which locates each pixel of every object in the image instead of the bounding boxes. It has two stages: region proposals and then classifying the proposals and generating bounding boxes and masks. It does so by using an additional fully convolutional network on top of a CNN based feature map with input as feature map and gives matrix with 1 on all locations where the pixel belongs to the object and 0 elsewhere as the output.

HOW TO USE?

python src/run.py -input input/3651581213_f81963d1dd_z.jpg -modelpath model/mask_rcnn_coco.h5 -output output USE run.py --help for help functions

usage

run.py [-h] [-input INPUT] [-modelpath MODELPATH] [-output OUTPUT]

Optional arguments:

 -h,  --help  show this help message and exit

 -input           INPUT                 Mention the input path

 -modelpath  MODELPATH      Mention the model path

 -output         OUTPUT             Mention the output path

WHAT ARE THE REQUIREMENTS?

To get all the requirements and dependencies installed run the command For GPU - pip install -r gpu_requirements.txt For CPU - pip install -r cpu_requirements.txt

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SHIVAM GARG
New Delhi, India
Level 41 74240 XP

2928 Profile
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A philosophy student cleverly disguised as a Coax Deep Learning engineer spending whole day, practically every day, experimenting with TensorFlow,Pytorch, and Caffe; dabbling with Python and C++; and drinking a wide variety of Coffee everyday.

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