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Collaborated with Davies Biological Sciences Lab at the University of New Brunswick to automate the manual sorting of 45k shadowgraph images captured underwater in the Bay of Fundy for research purposes. Utilized computer vision and deep learning to develop an ensemble model that achieved a 94.42% accuracy rate, striking an optimal False Positive-F

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ZooSegNet

BlogPost

BlogPost: click here

Commands to run

chmod +x setup_and_run.sh

./setup_and_run.sh

Run your Python script with the required arguments

chkpoint1- the location where model trained with the balanced dataset is saved or Model_v2_balanced_binary_best

chkpoint2- the location where model trained with all dataset is saved or Model_v2_binary_best

image_folder - Location where dataset is located or checkpoint_2023-08-03_01-35-29

save_dir - the location where you want to save the predictions

tensorboard_dir - the location where you want to save tensor board directory

is_onlysegmentation - Pass only if you want segmentation

with_cuda - Pass only when training on GPU

Run your bash script with following required arguments

python /home/ec2-user/SageMaker/FinalDemo/PredEnsembleSegmentationClassifier.py
--chkpoint1 "/home/ec2-user/SageMaker/FinalDemo/Model_v2_balanced_binary_best.pth.tar"
--chkpoint2 "/home/ec2-user/SageMaker/FinalDemo/Model_v2_binary_best.pth.tar"
--chkpoint_segmentation "/home/ec2-user/SageMaker/FinalDemo/checkpoint_2023-08-03_01-35-29.pt"
--image_folder "/home/ec2-user/SageMaker/FinalDemo/Dataset"
--save_dir "/home/ec2-user/SageMaker/FinalDemo/"
--segmented_save_dir "/home/ec2-user/SageMaker/FinalDemo/Detected/segmentedresults"
--segmented_data_dir "/home/ec2-user/SageMaker/FinalDemo/Detected"
--tensorboard_dir "/home/ec2-user/SageMaker/FinalDemo/tensorboard"
--with_cuda
--is_onlysegmentation # Uncomment the next line if you want to include the --is_onlysegmentation flag # --is_onlysegmentation

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Collaborated with Davies Biological Sciences Lab at the University of New Brunswick to automate the manual sorting of 45k shadowgraph images captured underwater in the Bay of Fundy for research purposes. Utilized computer vision and deep learning to develop an ensemble model that achieved a 94.42% accuracy rate, striking an optimal False Positive-F

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