license: cc-by-sa-4.0
language:
- en
phytoClassUCSC
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
Review section 4.1 of the model cards paper.
- Developed by the Kudela Lab from the Ocean Sciences Department at University of California, Santa Cruz.
- Current version trained in February, 2023.
- phytoClassUCSC-SoftNone02162023
- 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.
- Paper or other resource for more information
- Citation details
- License
- Email Patrick Daniel ([email protected]) for questions
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.
Primary intended users
IFCB users or researchers interested in phytoplankton ecology.
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.
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
This section is intended to demonstrate the ethical considerations that went into model development, surfacing ethical challenges and solutions to stakeholders. Ethical analysis does not always lead to precise solutions, but the process of ethical contemplation is worthwhile to inform on responsible practices and next steps in future work.
Review section 4.8 of the model cards paper.
Data
Human life
Mitigations
Risks and harms
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.