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COCO Dataset 2018 Stuff Segmentation Challenge
Internship project in Bennett University under Leadinginadi.ai
Getting Started
Problem Statement :-<br/> 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<br/> pip install tqdm<br/> pip install keras<br/> pip install keras-segmentation<br/>
Dataset format
You need to make two folders<br/>
Images Folder - For all the training images<br/> Annotations Folder - For the corresponding ground truth segmentation images<br/> The filenames of the annotation images should be same as the filenames of the RGB images.<br/>
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/" \
--n_classes=50
python -m keras_segmentation visualize_dataset \
--images_path="dataset1/images_prepped_train/" \
--segs_path="dataset1/annotations_prepped_train/" \
--n_classes=50
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 \
--model_name="pspnet"
Getting the predictions
python -m keras_segmentation predict \
--checkpoints_path="path_to_checkpoints" \
--input_path="dataset1/images_prepped_test/" \
--output_path="path_to_predictions"