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in Data Studio
YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/datasets-cards)
This model was trained on images of different types of plastic placed inside a tank connected to a 1080p webcam, located in the Ocean Technology Center at the University of Washington. The goal of this project is to enhance the detection and classification of various types of plastic debris commonly found in marine environments, providing valuable tools for environmental monitoring and research.
license: MIT
Dataset Details
The dataset consists of 4,511 images, capturing diverse types of plastic materials. Plastics were annotated using the YOLOv8 format to facilitate accurate object detection.
The types of plastic materials included are:
- Black Plastic Cap
- Blue Nitrile Glove
- Blue Plastic Cap
- Brown Multilayer Plastic
- Green Plastic Cap
- Orange Plastic Cap
- Plastic Bottle
- Purple Insulation Foam
- Purple Multilayer Plastic Bag
- Red-Orange BOPP Bag
- Red Cap
- Red Netting
- Red Plastic Straw
- Yellow Foam
- Yellow Rope
Pre-processing Steps
The following pre-processing steps were applied to each image:
- Auto-orientation of pixel data (with EXIF-orientation stripping) to ensure uniformity across all images.
- Resize to 640x360 (stretch) to standardize image dimensions for model training.
No additional image augmentation techniques were applied to this dataset.
Model Configuration
- Number of Classes (nc): 15
- Class Names: See the list above for detailed class names.
This model aims to support ongoing research in marine pollution by identifying plastic debris types effectively, helping researchers analyze patterns and develop mitigation strategies.
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