README.md

---
license: cc-by-sa-4.0
language:
- en
---
# phytoClassUCSC - A phytoplankton classifier for IFCB data

Note: Sections and prompts from the [model cards paper](https://arxiv.org/abs/1810.03993), v2.

Jump to section:

- [Model details](#model-details)
- [Intended use](#intended-use)
- [Factors](#factors)
- [Metrics](#metrics)
- [Evaluation data](#evaluation-data)
- [Training data](#training-data)
- [Quantitative analyses](#quantitative-analyses)
- [Ethical considerations](#ethical-considerations)
- [Caveats and recommendations](#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](https://arxiv.org/abs/1610.02357) 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 ([[email protected]]([email protected]))

## 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](https://arxiv.org/abs/1810.03993).

### Primary intended uses

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

### 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](https://arxiv.org/abs/1810.03993).

### 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](https://arxiv.org/abs/1810.03993).

### 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](https://arxiv.org/abs/1810.03993).

### Datasets

### Motivation

### Preprocessing

## Training data

Review section 4.6 of the [model cards paper](https://arxiv.org/abs/1810.03993).

## 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](https://arxiv.org/abs/1810.03993).

### 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](https://arxiv.org/abs/1810.03993).