Update README.md

README.md

@@ -1,3 +1,65 @@
 ---
 license: cc-by-4.0
+tags:
+- ocean
+- object-detection
+- trash
 ---
+
+# Trash Detector
+
+
+## Model Details
+
+- Trained by researchers at the Monterey Bay Aquarium Research Institute (MBARI).
+- Ultralytics YOLOv8x
+- Object detection model
+- Classes included in this detection model:
+  - trash
+  - eel
+  - rov
+  - starfish
+  - fish
+  - crab
+  - plant
+  - animal_misc
+  - shells
+  - bird
+  - shark
+  - jellyfish
+  - ray
+
+## Intended Use
+
+- Post-process video and images collected by marine researchers
+- This model should do a reasonable job detecting marine debris in a variety of habitats, depths, and lighting conditions.
+- 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
+
+## Metrics
+
+TODO
+
+## Training and Evaluation Data
+
+- Fine-tuned to detect 13 classes using training data combined from the following sources:
+  1. MBARI/FathomNet
+  2. trash-can: https://conservancy.umn.edu/handle/11299/214865
+  3. deep plastic: https://github.com/gautamtata/DeepPlastic
+  4. taco-dataset: https://tacodataset.org/
+  5. ocean agency image bank: https://www.theoceanagency.org/search-result?s=trash
+  6. Trash-ICRA19: https://conservancy.umn.edu/handle/11299/214366
+  7. roboflow aquarium dataset
+  8. roboflow Underwater Trash Detection.v5-dataset_v3
+- A compiled list of trash training data sets is here: https://github.com/AgaMiko/waste-datasets-review
+
+## Deployment
+
+1. Clone this repository
+2. In an environment with the ultralytics Python package installed, run:
+```bash
+yolo predict model=trash_mbari_09072023_640imgsz_50epochs_yolov8.pt
+```