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--- |
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license: cc-by-4.0 |
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tags: |
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- ocean |
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- object-detection |
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- trash |
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--- |
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# Trash Detector |
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## Model Details |
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- Trained by researchers at the Monterey Bay Aquarium Research Institute (MBARI). |
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- Ultralytics YOLOv8x |
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- Object detection model |
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- Classes included in this detection model: |
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- trash |
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- eel |
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- rov |
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- starfish |
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- fish |
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- crab |
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- plant |
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- animal_misc |
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- shells |
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- bird |
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- shark |
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- jellyfish |
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- ray |
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## Intended Use |
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- Post-process video and images collected by marine researchers |
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- This model should do a reasonable job detecting marine debris in a variety of habitats, depths, and lighting conditions. |
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- Can be used to build a localized set of training images, when neither training data nor a model exists for the imagery being analyzed. |
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## Factors |
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- Distribution shifts related to sampling platform, camera parameters, illumination, and deployment environment are expected to impact model performance |
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## Metrics |
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TODO |
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## Training and Evaluation Data |
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- Fine-tuned to detect 13 classes using training data combined from the following sources: |
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1. MBARI/FathomNet |
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2. trash-can: https://conservancy.umn.edu/handle/11299/214865 |
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3. deep plastic: https://github.com/gautamtata/DeepPlastic |
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4. taco-dataset: https://tacodataset.org/ |
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5. ocean agency image bank: https://www.theoceanagency.org/search-result?s=trash |
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6. Trash-ICRA19: https://conservancy.umn.edu/handle/11299/214366 |
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7. roboflow aquarium dataset |
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8. roboflow Underwater Trash Detection.v5-dataset_v3 |
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- A compiled list of trash training data sets is here: https://github.com/AgaMiko/waste-datasets-review |
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## Deployment |
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1. Clone this repository |
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2. In an environment with the ultralytics Python package installed, run: |
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```bash |
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yolo predict model=trash_mbari_09072023_640imgsz_50epochs_yolov8.pt |
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``` |
