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There are three levels in image analysis, Classification, Detection and Segmentation. Image segmentation is the division of an image into regions or categories. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.
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Image Segmtation on COCO Dataset

COCO Dataset 2018 Stuff Segmentation Challenge

Internship project in Bennett University under

Getting Started

Problem Statement :-
To perform Semantic Segmentation of Stuff classes.The COCO Stuff Segmentation Task is designed to push the state of the art in semantic segmentation of stuff classes.

Prerequisites and installing

pip install tensorflow-gpu
pip install tqdm
pip install keras
pip install keras-segmentation

Dataset format

You need to make two folders

Images Folder - For all the training images
Annotations Folder - For the corresponding ground truth segmentation images
The filenames of the annotation images should be same as the filenames of the RGB images.

Usage via command line

Visualizing the prepared data

python -m keras_segmentation verify_dataset \
 --images_path="dataset1/images_prepped_train/" \
 --segs_path="dataset1/annotations_prepped_train/"  \
python -m keras_segmentation visualize_dataset \
 --images_path="dataset1/images_prepped_train/" \
 --segs_path="dataset1/annotations_prepped_train/"  \

Training the Model

python -m keras_segmentation train \
--checkpoints_path="path_to_checkpoints" \
--train_images="dataset1/images_prepped_train/" \
--train_annotations="dataset1/annotations_prepped_train/" \
--val_images="dataset1/images_prepped_test/" \
--val_annotations="dataset1/annotations_prepped_test/" \
--n_classes=300 \
--input_height=320 \
--input_width=640 \

Getting the predictions

python -m keras_segmentation predict \
 --checkpoints_path="path_to_checkpoints" \
 --input_path="dataset1/images_prepped_test/" \


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