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AI Fish recognition platform










2019 - present


Business needs

  • Platform for the scientific marine and angler communities
  • A tool to attain photos and build AI models for fish recognition
  • First in the world the most complete open-source database of fish
  • Online and mobile access

What we did

Fishial.AI was created by private foundation with mission to become the world’s largest marine biodiversity-related citizen science project focused on fish identification. With them, we have designed and implemented a unified platform for the cooperation of various entities to create the world's largest training database for fish pictures.

We have prepared and created an editor supported by AI/Machine Learning/Computer Vision technology that allows for automatic shape determination and fish attribute identification. We have developed AI/Machine Learning models that support the editor and create polygons describing the fish in order to optimize photo classification time. The platform's development plan includes using the acquired photo database to build further AI models recognizing any species of fish in the world.

Dedicated for

  • Anglers
  • Marine Biologists
  • Scientific communities
  • Universities
  • Government institutions
  • Citizen volunteers

Key features

  • Advanced AI-enhanced photo editor
  • Complex user structure for review process
  • Comprehensive portal summary statistics for admins, editors and team owners
  • CSV batch import for one-click upload of several hundreds of photos
  • ML-based fish and species recognition
  • Manual and automatic datasets exports
  • Open and public teams for volunteers
  • Intuitive portal and editor tools and wizards for beginners with complete documentation
  • Blurring faces

Machine learning

The Fishial project includes a part that is responsible for detecting fish and drawing its outlines. This algorithm is implemented as part of the ML side project, the following things have been done:

  1. 1.Collecting data for training. To train the neural network, it was necessary to prepare a dataset, we decided to find not only fish but also its contours for better subsequent identification of species. We were able to significantly increase the dataset in a short time and without large resources with other techniques.
  2. 2.Neural network training. We used the framework to train the neural network and during the tests, the final version was chosen with pre-trained weights on the COCO dataset.
  3. 3.The next step was to deploy the model. We chose the Azure ML service, which provides an easy way to deploy the model, a choice of a virtual machine with a graphics card and easy scaling.
  4. 4.Additionally, to improve the performance of researchers who will annotate the images, an algorithm was proposed to find the contours of the fish by highlighting only its bounding box. Thus, the user will not spend a lot of time sketching not found fish.

Cloud native

To enable seamless application development and high performance, we relied on microservices managed by Kubernetes. The architecture was prepared for processing millions of photos, so we used two cloud providers:

  1. 1.Google Cloud hosts the portal along with the photos
  2. 2.Microsoft Azure hosts the ML server for fish detection


  • Cost and time reduction of photos analysis thanks to AI/Machine Learning/Computer Vision algorithms
  • Easy of use, for non IT person, photos editor online tool
  • The project was recognized by the Con X Tech Prize jury and qualified for the group of 20 finalists of the Con X Tech Prize competition

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