phytoClassUCSC - A phytoplankton classifier for IFCB data

Note: Sections and prompts from the model cards paper, v2.

Jump to section:

• Model details
• Intended use
• Factors
• Metrics
• Evaluation data
• Training data
• Quantitative analyses
• Ethical considerations
• Caveats and recommendations

Model details

• Developed by the Kudela Lab from the Ocean Sciences Department at University of California, Santa Cruz.
• Current version trained in February, 2023.
• Version 1.0
• phytoClassUCSC is a depthwise- CNN based on the Xception architecture Chollet, F., 2017 with 134 layers using weights pretrained on ImageNet.
• An average pooling layer is used.
• Licensed under CC-BY-SA-4.0
• For Questions email Patrick Daniel (pcdaniel@ucsc.edu)

Intended use

This model was designed and trained to work with IFCB data generated in Monterey Bay. While that does not mean it may not perform well in other locations, the distribution of training images reflects common phytoplankton observed at the Santa Cruz Wharf and Power Buoy locations.

Independent model validation should be used when applying the model to other sites.

Review section 4.2 of the model cards paper.

Primary intended uses

Generalized phytoplankton classifier for common taxa found in the Monterey Bay. This

Primary intended users

Researchers intersted in a general.

Out-of-scope use cases

Observing and identifying rare or non-endemic taxa.

Factors

Model classes were chosen based on common and resolvable phytoplankton taxa. Taxonomic groupings were chosen based on what researchers in the lab felt groups that could be confidently identified, given the expertise and research intersts of the lab.

Instrument

Model was trained on images from Imaging FlowCytobot (IFCB) instruments primary deployed at the Santa Cruz Wharf and the Monterey Bay Aquarium Research Institute (MBARI) Power Buoy. The Santa Cruz Wharf IFCB (#104) is an early generation

Review section 4.3 of the model cards paper.

Relevant factors

Evaluation factors

Metrics

The appropriate metrics to feature in a model card depend on the type of model that is being tested. For example, classification systems in which the primary output is a class label differ significantly from systems whose primary output is a score. In all cases, the reported metrics should be determined based on the model’s structure and intended use.

Review section 4.4 of the model cards paper.

Model performance measures

Decision thresholds

Approaches to uncertainty and variability

Evaluation data

All referenced datasets would ideally point to any set of documents that provide visibility into the source and composition of the dataset. Evaluation datasets should include datasets that are publicly available for third-party use. These could be existing datasets or new ones provided alongside the model card analyses to enable further benchmarking.

Review section 4.5 of the model cards paper.

Datasets

Motivation

Preprocessing

Training data

Review section 4.6 of the model cards paper.

Quantitative analyses

Quantitative analyses should be disaggregated, that is, broken down by the chosen factors. Quantitative analyses should provide the results of evaluating the model according to the chosen metrics, providing confidence interval values when possible.

Review section 4.7 of the model cards paper.

Unitary results

Intersectional result

Ethical considerations

None

Data

Use cases

Caveats and recommendations

This section should list additional concerns that were not covered in the previous sections.

Review section 4.9 of the model cards paper.