Init README

README.md

@@ -1,3 +1,39 @@
 ---
 license: cc-by-4.0
+tags:
+- ocean
+- benthic
+- object-detection
 ---
+
+# FathomNet2023 Baseline Model
+
+## Model Details
+- Trained by researchers at [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) (MBARI) as a baseline for the [FathomNet2023 Competition](https://www.kaggle.com/competitions/fathomnet-out-of-sample-detection/overview) presented with the [Fine Grained Visual Categorization workshop](https://sites.google.com/view/fgvc10/home) at CVPR 2023.
+- [Ultralytics YOLOv8.0.117](https://github.com/ultralytics/ultralytics/pull/3145)
+- Object detection
+- Fine tuned yolov8m to detect 290 fine grained taxonmic categories of benthic animals in the Greater Monterey Bay Area off the coast of Central California.
+
+## Intended Use
+- Make detections on images collect on the sea floor in the Monterey Bay Area.
+
+## 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 can be found on the [competition Kaggle page](https://www.kaggle.com/competitions/fathomnet-out-of-sample-detection/overview).
+
+## 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 = 0.33515
+- Performance is quite variable depending on the target organism even when testing on in-distribution data.
+
+## Training and Evaluation Data
+- Training data is the [FathomNet2023 competition split](https://www.kaggle.com/competitions/fathomnet-out-of-sample-detection/overview) and internal MBARI data
+- Class labels have a [long tail and localizations occur throughout the frame]().
+
+## Deployment
+In an environment running YOLOv8:
+
+```
+python classify/predict.py --weights best.pt --data data/images/
+```