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---
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
``` |