ai4-metadata.yml

metadata_version: 2.0.0
title:  AI4life model loader
summary: "Support for inference of the AI4LIFE model on the marketplace."
description: |-
  The [BioImage Model Zoo](https://bioimage.io/#/) is a community-driven platform that
  provides standardized deep learning models for bioimage analysis.

  This module integrates models from the **BioImage.IO** package into the **AI4EOSC** Marketplace
  dashboard, specifically those using PyTorch weights and following the v0.5 format.
  The module allows users to seamlessly explore, deploy, and utilize these models within
  the AI4EOSC ecosystem, providing a user-friendly interface for advanced bioimage analysis.

  **Key Features**
  - Model Discovery: Automatically fetch and list models available in BioImage.IO that meet the criteria (PyTorch weights, v0.5 format).

  - Metadata Visualization: Display essential information about each model,
   such as input/output specifications, authors, license details, and documentation.

  - Seamless Deployment: Enable one-click deployment of models to AI4EOSC compute resources.

  - Model Preview: Provide an interactive preview to test models on sample data directly in the dashboard.

  **References**:
  - BioImage.Io github repository: https://github.com/bioimage-io/core-bioimage-io-python
  - Documentation: https://bioimage-io.github.io/core-bioimage-io-python/bioimageio/core
dates:
  created: '2024-11-11'
  updated: '2024-11-11'
links:
  ai4_template: ai4-template-adv/2.0.1
  source_code: https://github.com/ai4os/ai4os-ai4life-loader
  docker_image: ai4oshub/ai4os-ai4life-loader
  # documentation: http://add-some-documentation.com
  # dataset: http://add-some-url-pointing-to-your-dataset.com
  # weights: http://add-some-weights-url.com
  # citation: http://add-some-DOI-url.com
  # base_model: http://add-some-link-to-another-model.com
tags:        # required property, add user-defined tags that you consider relevant
   - deep learning
   - segmentation
   - 2D input
   - 3D input
   - UNet
   - Segment Anything
   - cellpose
tasks:       # required property, uncomment together with relevant items
   - Computer Vision
   - Classification
categories:  # required property, uncomment together with relevant items
   - AI4 inference
   - AI4 tools
libraries:   # required property, uncomment together with relevant items
   - PyTorch
   - Scikit-learn
data-type:   # optional, uncomment together with relevant items
   - Image