Difference between revisions of "Training Projects"
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[https://drive.google.com/file/d/1t1DqNL6LZOiHzbrkxIZbzQlD-PVVaoqG/view?usp=sharing '''Buoy Location'''] | [https://drive.google.com/file/d/1t1DqNL6LZOiHzbrkxIZbzQlD-PVVaoqG/view?usp=sharing '''Buoy Location'''] | ||
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| + | You can find the instructions to clean the dataset, merging of files and traing the ML models from the below link: | ||
[https://drive.google.com/file/d/1sr7QFHAycJ7KRBXuPgxgFDLxvHQvyngz/view?usp=sharing '''Instructions for Cleaning/Merging/Training'''] | [https://drive.google.com/file/d/1sr7QFHAycJ7KRBXuPgxgFDLxvHQvyngz/view?usp=sharing '''Instructions for Cleaning/Merging/Training'''] | ||
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| + | We have implemented the code on IBM Watson Cloud and encourage you to use this to get the experience of Cloud. Below link will provide you the instrcutions for using the IBM Watson Cloud. The Lite version of this cloud is free and provide you 25GB storage which is enough for this project. | ||
[https://drive.google.com/file/d/1ns7OrUy4JURdv5QwD3vjGrZszU0DQ3wa/view?usp=sharing '''Instructions for using IBM Watson Cloud'''] | [https://drive.google.com/file/d/1ns7OrUy4JURdv5QwD3vjGrZszU0DQ3wa/view?usp=sharing '''Instructions for using IBM Watson Cloud'''] | ||
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| + | We have created notebooks with the code for your reference in the below link. | ||
[https://drive.google.com/file/d/1WpFhgKkoq19k4XR0dxp1wxgXFsPPnMgx/view?usp=sharing '''Links for Notebooks'''] | [https://drive.google.com/file/d/1WpFhgKkoq19k4XR0dxp1wxgXFsPPnMgx/view?usp=sharing '''Links for Notebooks'''] | ||
Revision as of 13:42, 6 May 2021
DeepSense has compiled a few data sets for students, and others interested in the ocean and AI, so they can have the opportunity to complete AI projects independently. We hope participants can learn about a specific type of ocean related data, and experience an explicit AI project. It is expected that the participants work on the project alone, but we have provided some guidance that includes notebooks, data, outputs and models to try to improve upon.
We have found that the data cleaning step can take a long time, so our hope is that these datasets will be reasonably clean, allowing the participants to explore ocean AI.
Object Detection
We used the google open images database to obtain approximately 650 images of starfish. The images were already separated into train, test and validation sets. The metadata linked below is only for the starfish images, not for the entire dataset. The metadata includes coordinates for bounding boxes around the starfish.
If you want to download other categories of images from the open images database, you can do so by following the instructions here:
Regression
Predicting the values of one buoy using the parameters of another buoy. In this project, we are using the dataset of Mouth of Placentia Bay Buoy, Pilot Boarding Station / Red Island Shoal Buoy, Placentia Bay: Ragged Islands – KLUMI( Land station) which are located in Newfoundland and Labrador.
Datasets
Pilot Boarding Station / Red Island Shoal Buoy
Placentia Bay: Ragged Islands – KLUMI( Land station)
The dataset available here is till April 19, 2021. You can get the latest dataset from smartatlantic.
You can find the instructions to clean the dataset, merging of files and traing the ML models from the below link:
Instructions for Cleaning/Merging/Training
We have implemented the code on IBM Watson Cloud and encourage you to use this to get the experience of Cloud. Below link will provide you the instrcutions for using the IBM Watson Cloud. The Lite version of this cloud is free and provide you 25GB storage which is enough for this project.
Instructions for using IBM Watson Cloud
We have created notebooks with the code for your reference in the below link.
REFERENCES
A MACHINE LEARNING REDUNDANCY MODEL FOR THE HERRING COVE SMART BUOY
