---
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
```