| --- |
| license: cc-by-4.0 |
| tags: |
| - ocean |
| - object-detection |
| - trash |
| library_name: yolov26 |
| --- |
| |
| # 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 |
| ``` |
