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
run.py --help for help functions
run.py [-h] [-input INPUT] [-modelpath MODELPATH] [-output OUTPUT]
-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
Author View Profile
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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