Every year, millions of tons of plastic spill from our rivers to our oceans. A portion of this plastic travels to ocean garbage patches, where it gets caught in circulating currents. These garbage patches are a heavy burden to our oceanic life and if no action is taken, plastic will increasingly impact our ecosystems, health, and economies.
The Ocean Cleanup’s mission is to close the sources of plastic pollution & clean up what has already accumulated in the ocean in order to remove 90 % of floating ocean plastic. To do that, The Ocean Cleanup relies on technology to identify, classify & locate the garbage patches & their content.
The current datasets of The Ocean Cleanup were built using conventional methods (trawls) that are very labor-intensive, or less conventional methods (airplane) that are very costly and complex to organize. The team has worked on an AI object detection software, combined with automated time-lapse image series along GPS-tagged transects. This creates a remote sensing approach to detect and map the dynamic behavior of floating ocean plastic more efficiently. Ultimately, this growing dataset will help The Ocean Cleanup determine where to deploy cleanup in an extensive area with an uneven distribution of plastic debris.
Deep dive about Ocean Cleanup’s AI research in this article.
To help The Ocean Cleanup in their mission, we are organising a data-science challenge during which we’re mobilising hundreds of data scientists to annotate a massive dataset of polluted ocean photographs.
We Want You To Join!
From December 5th to 9th, participants in the challenge will annotate images from The Ocean Cleanup’s on-board cameras, classifying the detected objects. With a mix of human and model annotation to accelerate the labeling process, your goal will be to annotate efficiently to create the highest quality dataset.
To help you get the best results, we’ve partnered with great data-science tools to help you be as successful as possible:
Weights & biases
Weights & Biases helps your ML team unlock their productivity by optimizing, visualizing, collaborating on, and standardizing their model and data pipelines – regardless of framework, environment, or workflow.
Track everything you need to make your models reproducible with Weights & Biases— from hyperparameters and code to model weights and dataset versions.
OVHcloud helps data scientists and ML teams to easily start notebooks or trainings in minutes. Our AI-managed services allow you to get the right tools at each step of your AI projects, so you can focus on your data skills instead of infra complexity, share easily with your team, manage datasets securely and accelerate your model delivery.
Isahit is an Ethical On-demand Workforce Platform for digital tasks, certified B Corp. We offer you to Build, Train & Deploy a custom and diverse workforce on all your wide digital tasks projects.
Thanks to our diverse & skilled workforce, powerful in-house, partners labeling tools, and efficient workflow, we cover issues across many industries and address a wide range of use cases: from skin recognition to food to predictive maintenance and guarantee 100% accuracy for your models.
How Will We Annotate 180,000+ Assets?
During the challenge, Kili Technology and our partners put at your disposal:
Kili Technology's labeling platform to massively pre-annotate, manually annotate as teams & check the quality of annotation
OVHcloud GPUs to run the pre-annotation and annotation error detection models
Weights and Biases's platform to track the performance of your pre-annotation models
By participating in this challenge, you’ll be able to:
discover a passionate community of data-scientists committed to fixing the ecological impacts of our economy
develop your skillset on a suite of new data-science tools
participate in the annotation of 180,000+ images of plastic in oceans
Winners of the challenge will be determined on the efficiency & quality of the labeling, and will be rewarded with extensive free vouchers for all solutions and swag packs!
Participants can join as teams or as single contributors. Beginners are welcome.
Register here to participate, and see you soon!