Write first cut of model card

README.md

@@ -1,3 +1,45 @@
 ---
 license: cc-by-4.0
+tags:
+  - ocean
+  - object-detection
+  - object-localization
+  - single-class
 ---
+
+# FathomNet Megalodon Detector
+
+## Model Details
+
+- Trained by researchers at the [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) (MBARI).
+- Ultralytics [YOLOv8x](https://github.com/ultralytics/ultralytics)
+- Object detection model
+- Fine-tuned to detect 1 class, called 'object', using all FathomNet localizations
+
+## Intended Use
+
+- Post-process video and images collected by marine researchers
+- Can be used to build a localized set of training images, when neither training data nor a model exists for the imagery being analyzed
+
+## Factors
+
+- Distribution shifts related to sampling platform, camera parameters, illumination, and deployment environment are expected to impact model performance
+- Evaluation was performed on an IID subset of available training data as well as out-of-distribution data
+
+## Metrics
+
+- [Normalized confusion matrix](plots/confusion_matrix_normalized.png), [precision-recall curve](plots/PR_curve.png), and [F1-confidence curve](plots/F1_curve.png) were evaluated at test time
+- mAP@0.5 = 0.782
+
+## Training and Evaluation Data
+
+- All publicly-available data on [FathomNet](https://fathomnet.org/)
+
+## Deployment
+
+1. Clone this repository
+2. In an environment with the [`ultralytics` Python package](https://github.com/ultralytics/ultralytics) installed, run:
+
+```bash
+yolo predict model=best.pt
+```