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## Model Details
### Model Description
A pretrained, general purpose microscopy identity tracking model. Trained on diverse data across scales, from cells to organelles.
Expects videos with accompanying detection labels in .slp or CellTrackingChallenge format.
- **Developed by:** talmolab
- **Shared by:** talmolab
- **License:** [More Information Needed]
### Model Sources
- **Repository:** [talmolab/dreem](https://github.com/talmolab/dreem)
- **Paper [optional]:** [More Information Needed]
## Uses
To track identities of instances in microscopy timelapses. Maintains consistent identities across time.
## How to Get Started with the Model
See https://dreem.sleap.ai
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
See configuration files provided in model directory
#### Speeds, Sizes, Times [optional]
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[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
CLEARMOT metrics, implemented by https://github.com/cheind/py-motmetrics
CellTrackingChallenge metrics, implemented by https://github.com/CellTrackingChallenge/py-ctcmetrics#
### Results
[More Information Needed]
#### Summary
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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