32 lines
1021 B
Markdown
32 lines
1021 B
Markdown
# Image and Video Understanding Project
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This project compares two deep learning models for instance segmentation on waste detection: Mask R-CNN (using Detectron2) and YOLOv8.
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## Project Structure
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- **MRCNN/**: Mask R-CNN implementation using Detectron2
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- Training and evaluation code in `main.ipynb`
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- Trained models and results in `results/`
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- **YOLO/**: YOLOv8 segmentation implementation
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- Training and evaluation code in `main.ipynb`
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- Trained models and results in `results/`
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## Dataset
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Both models are trained on the TACO (Trash Annotations in Context) dataset with 20 classes of waste objects including:
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- Plastic bottles, glass bottles, bottle caps
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- Drink cans, paper cups, cartons
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- Plastic film, wrappers, straws
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- Cigarettes, and other litter items
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## Models
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- **Mask R-CNN**: ResNet-101 backbone with Feature Pyramid Network
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- **YOLOv8**: Large segmentation model (YOLOv8l-seg)
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## Results
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Training and evaluation results are stored in the respective `results/` directories for each model.
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