Instance-Segmentation

Project done in Instance Segmentation and Human pose estimation.

Instance segmentation requires the prediction of object instances and their per-pixel segmentation mask.

• This makes it a hybrid of semantic segmentation and object detection.

• I installed Detectron2 in google colab. I used pictures(images) from my personal collections and on the internet and run a pre-trained model on them.

Summary of Work

• Instance segmentation is a subtype of image segmentation which identifies each instance of each object within the image at the pixel level.

• I used the Mask Regional Convolutional Neural Network (Mask R-CNN) method. The framework of Mask R-CNN is based on two stages: first, it scans the im- age to generate proposals; which are areas with a high likelihood to contain an object.

• Second, it classifies these proposals and creates bounding boxes and masks.

• The backbone architecture used here is the ResNet-50 . The data set used is the coco dateset.

Important Information

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-Used Python Version: 3.6.0

-install  modules with ```pip install detectron2``` command.

-Run the file CV_2LAB_1 in colab or jupyter notebook.

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