Image and Video Understanding Project

A comprehensive project comparing multiple state-of-the-art deep learning models for object detection and instance segmentation on a waste/litter detection dataset.

Overview

This project evaluates and compares different deep learning architectures for instance segmentation on a custom waste detection dataset. Each model is trained and evaluated on the same dataset to enable fair comparison.

Models

1. YOLO (YOLOv8l-seg)

• Model: YOLOv8 Large Segmentation
• Framework: Ultralytics
• Parameters: 45.9M
• Training: 200 epochs, batch size 16, image size 960x960
• Features: Real-time inference, bounding box + mask prediction

2. Mask R-CNN

• Backbone: ResNet-101 with FPN
• Framework: Detectron2
• Training: 1000-3000 iterations, batch size 8, image size 960x960
• Features: Instance segmentation with high accuracy

3. Mask2Former

• Architecture: Transformer-based segmentation
• Framework: Detectron2
• Features: Unified framework for semantic, instance, and panoptic segmentation

4. DETR

• Status: Dataset prepared (implementation in progress)

Dataset

Custom waste/litter detection dataset with 20 classes:

• Clear plastic bottle, Glass bottle, Plastic bottle cap, Metal bottle cap
• Broken glass, Drink can, Other carton, Corrugated carton
• Paper cup, Disposable plastic cup, Plastic lid, Other plastic
• Normal paper, Plastic film, Other plastic wrapper, Pop tab
• Plastic straw, Styrofoam piece, Unlabeled litter, Cigarette

Dataset Structure: Train/Val/Test splits in COCO format

Project Structure

├── YOLO/
│   ├── main.ipynb                    # Training and evaluation notebook
│   ├── results/
│   │   ├── train_200_960_16/        # Training outputs
│   │   └── evaluation_200_960_16/   # Evaluation results
│   └── dataset/                      # Dataset configuration
├── MRCNN/
│   ├── main.ipynb                    # Training and evaluation notebook
│   ├── results/
│   │   ├── train_1000_iter/         # Training outputs
│   │   └── eval/                     # Evaluation metrics
│   └── requirements.txt
├── M2FORMER/
│   ├── main.ipynb                    # Training and evaluation notebook
│   ├── output/                       # Training outputs
│   ├── Mask2Former/                  # Mask2Former repository
│   └── requirements.txt
└── DETR/
    └── dataset/                      # Image data

Setup

Prerequisites

• Python 3.8+
• PyTorch (with CUDA support recommended)
• GPU recommended for training

Installation

YOLO

pip install ultralytics

Mask R-CNN

pip install -r MRCNN/requirements.txt
pip install 'git+https://github.com/facebookresearch/detectron2.git'

Mask2Former

pip install -r M2FORMER/requirements.txt
pip install 'git+https://github.com/facebookresearch/detectron2.git'
git clone https://github.com/facebookresearch/Mask2Former.git
cd Mask2Former/mask2former/modeling/pixel_decoder/ops/
./make.sh  # Compile CUDA operations

Usage

Training

Each model has a Jupyter notebook (main.ipynb) with complete training pipelines:

1. YOLO: Open YOLO/main.ipynb

   • Configure dataset path in data.yaml
   • Run training cells
   • Model saves checkpoints every 10 epochs

2. Mask R-CNN: Open MRCNN/main.ipynb

   • Configure dataset paths and parameters
   • Register COCO format datasets
   • Train and evaluate

3. Mask2Former: Open M2FORMER/main.ipynb

   • Setup Mask2Former repository
   • Configure training parameters
   • Train and evaluate

Evaluation

All notebooks include:

• COCO-style evaluation metrics
• Confusion matrix generation
• Prediction visualization
• Performance comparison tools

Results

YOLO Results

• Box mAP50: 26.9%
• Box mAP50-95: 20.7%
• Mask mAP50: 26.7%
• Mask mAP50-95: 19.5%
• Precision (Box): 28.8%
• Recall (Box): 29.5%

Mask R-CNN Results

• Box AP: 15.8%
• Box AP50: 23.9%
• Mask AP: 15.9%
• Mask AP50: 23.7%
• Best performance on: Metal bottle cap (50.4% AP), Clear plastic bottle (42.6% AP), Drink can (40.1% AP)

Results are saved in respective results/ directories with:

• Model weights (.pth or .pt files)
• Evaluation metrics (JSON format)
• Training logs and visualizations
• Confusion matrices

Training Parameters

YOLO

• Epochs: 200
• Batch size: 16
• Image size: 960x960
• Learning rate: 0.01
• Optimizer: AdamW
• Data augmentation: Enabled

Mask R-CNN

• Iterations: 1000-3000
• Batch size: 8
• Image size: 960x960
• Learning rate: 0.00025
• Backbone: ResNet-101 FPN
• ROI batch size: 16

Mask2Former

• Configuration: COCO instance segmentation
• Backbone: ResNet-101
• Image size: Variable

Requirements

Common Dependencies

• Python 3.8+
• PyTorch
• CUDA (for GPU training)
• OpenCV
• NumPy
• Matplotlib

Model-Specific

See individual requirements.txt files in each model directory for complete dependency lists.