Skeleton model card

README.md

@@ -1,3 +1,42 @@
 ---
 license: cc-by-4.0
+tags:
+- ocean
+- midwater
+- benthic
+- object-detection
 ---
+
+# MBARI Monterey Bay 315k YOLOv5
+
+<!-- TODO: Fill out the model card
+## Model Details
+- Trained by researchers at [CVisionAI](https://www.cvisionai.com/) and the [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) (MBARI).
+- [YOLOv5v6.2](https://github.com/ultralytics/yolov5/tree/v6.2)
+- Object detection
+- Fine tuned yolov5l to detect 22 morhpotaxonmic categories of midwater animals in the Greater Monterey Bay Area off the coast of Central California.
+
+## Intended Use
+- Make real time detections on video feed from MBARI Remotely Operated Vehicles.
+- Post-process video collected in the region by MBARI vehicles.
+
+## 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. Data to test out of distribution performance not currently available.
+
+## Metrics
+- [Precision-Recall curve](https://huggingface.co/FathomNet/MBARI-midwater-supercategory-detector/blob/main/plots/PR_curve.png) and [per class accuracy]((https://huggingface.co/FathomNet/MBARI-midwater-supercategory-detector/blob/main/plots/confusion_matrix.png)) were evaluated at test time. 
+- mAP@0.5 = 0.866
+- Indicates reasonably good performance for target task.
+
+## Training and Evaluation Data
+- A combination of publicly available [FathomNet](https://fathomnet.org/fathomnet/#/) and internal MBARI data
+- Class labels have a [long tail and localizations occur throughout the frame](https://huggingface.co/FathomNet/MBARI-midwater-supercategory-detector/blob/main/plots/labels.jpg).
+
+## Deployment
+In an environment running [YOLOv5v6.2](https://github.com/ultralytics/yolov5/tree/v6.2):
+
+```
+python classify/predict.py --weights best.pt --data data/images/
+```
+-->