2025-12-10 12:08:54 +02:00
33 changed files with 4240 additions and 90 deletions
--- a/ARCHITECTURE.md
+++ b/ARCHITECTURE.md
@@ -2,11 +2,11 @@

 ## Project Overview

-A desktop application for detecting organelles and membrane branching structures in microscopy images using YOLOv8s, with comprehensive training, validation, and visualization capabilities.
+A desktop application for detecting and segmenting organelles and membrane branching structures in microscopy images using YOLOv8s-seg, with comprehensive training, validation, and visualization capabilities including pixel-accurate segmentation masks.

 ## Technology Stack

- **ML Framework**: Ultralytics YOLOv8 (YOLOv8s.pt model)
+- **ML Framework**: Ultralytics YOLOv8 (YOLOv8s-seg.pt segmentation model)
 - **GUI Framework**: PySide6 (Qt6 for Python)
 - **Visualization**: pyqtgraph
 - **Database**: SQLite3
@@ -110,6 +110,7 @@ erDiagram
        float x_max
        float y_max
        float confidence
+        text segmentation_mask
        datetime detected_at
        json metadata
    }
@@ -122,6 +123,7 @@ erDiagram
        float y_min
        float x_max
        float y_max
+        text segmentation_mask
        string annotator
        datetime created_at
        boolean verified
@@ -139,7 +141,7 @@ Stores information about trained models and their versions.
 | model_name | TEXT | NOT NULL | User-friendly model name |
 | model_version | TEXT | NOT NULL | Version string (e.g., "v1.0") |
 | model_path | TEXT | NOT NULL | Path to model weights file |
-| base_model | TEXT | NOT NULL | Base model used (e.g., "yolov8s.pt") |
+| base_model | TEXT | NOT NULL | Base model used (e.g., "yolov8s-seg.pt") |
 | created_at | TIMESTAMP | DEFAULT CURRENT_TIMESTAMP | Model creation timestamp |
 | training_params | JSON | | Training hyperparameters |
 | metrics | JSON | | Validation metrics (mAP, precision, recall) |
@@ -159,7 +161,7 @@ Stores metadata about microscopy images.
 | checksum | TEXT | | MD5 hash for integrity verification |

 #### **detections** table
-Stores object detection results.
+Stores object detection results with optional segmentation masks.

 | Column | Type | Constraints | Description |
 |--------|------|-------------|-------------|
@@ -172,11 +174,12 @@ Stores object detection results.
 | x_max | REAL | NOT NULL | Bounding box right coordinate (normalized 0-1) |
 | y_max | REAL | NOT NULL | Bounding box bottom coordinate (normalized 0-1) |
 | confidence | REAL | NOT NULL | Detection confidence score (0-1) |
+| segmentation_mask | TEXT | | JSON array of polygon coordinates [[x1,y1], [x2,y2], ...] (normalized 0-1) |
 | detected_at | TIMESTAMP | DEFAULT CURRENT_TIMESTAMP | When detection was performed |
 | metadata | JSON | | Additional metadata (processing time, etc.) |

 #### **annotations** table
-Stores manual annotations for training data (future feature).
+Stores manual annotations for training data with optional segmentation masks (future feature).

 | Column | Type | Constraints | Description |
 |--------|------|-------------|-------------|
@@ -187,6 +190,7 @@ Stores manual annotations for training data (future feature).
 | y_min | REAL | NOT NULL | Bounding box top coordinate (normalized) |
 | x_max | REAL | NOT NULL | Bounding box right coordinate (normalized) |
 | y_max | REAL | NOT NULL | Bounding box bottom coordinate (normalized) |
+| segmentation_mask | TEXT | | JSON array of polygon coordinates [[x1,y1], [x2,y2], ...] (normalized 0-1) |
 | annotator | TEXT | | Name of person who created annotation |
 | created_at | TIMESTAMP | DEFAULT CURRENT_TIMESTAMP | Annotation timestamp |
 | verified | BOOLEAN | DEFAULT 0 | Whether annotation is verified |
@@ -245,8 +249,9 @@ graph TB
 ### Key Components

 #### 1. **YOLO Wrapper** ([`src/model/yolo_wrapper.py`](src/model/yolo_wrapper.py))
-Encapsulates YOLOv8 operations:
- Load pre-trained YOLOv8s model
+Encapsulates YOLOv8-seg operations:
+- Load pre-trained YOLOv8s-seg segmentation model
+- Extract pixel-accurate segmentation masks
 - Fine-tune on custom microscopy dataset
 - Export trained models
 - Provide training progress callbacks
@@ -255,10 +260,10 @@ Encapsulates YOLOv8 operations:
 **Key Methods:**
 ```python
 class YOLOWrapper:
-    def __init__(self, model_path: str = "yolov8s.pt")
+    def __init__(self, model_path: str = "yolov8s-seg.pt")
    def train(self, data_yaml: str, epochs: int, callbacks: dict)
    def validate(self, data_yaml: str) -> dict
-    def predict(self, image_path: str, conf: float) -> list
+    def predict(self, image_path: str, conf: float) -> list  # Returns detections with segmentation masks
    def export_model(self, format: str, output_path: str)
 ```

@@ -435,7 +440,7 @@ image_repository:
  allowed_extensions: [".jpg", ".jpeg", ".png", ".tif", ".tiff"]

 models:
-  default_base_model: "yolov8s.pt"
+  default_base_model: "yolov8s-seg.pt"
  models_directory: "data/models"

 training:
--- a/BUILD.md
+++ b/BUILD.md
@@ -0,0 +1,178 @@
+# Building and Publishing Guide
+
+This guide explains how to build and publish the microscopy-object-detection package.
+
+## Prerequisites
+
+```bash
+pip install build twine
+```
+
+## Building the Package
+
+### 1. Clean Previous Builds
+
+```bash
+rm -rf build/ dist/ *.egg-info
+```
+
+### 2. Build Distribution Archives
+
+```bash
+python -m build
+```
+
+This will create both wheel (`.whl`) and source distribution (`.tar.gz`) in the `dist/` directory.
+
+### 3. Verify the Build
+
+```bash
+ls dist/
+# Should show:
+# microscopy_object_detection-1.0.0-py3-none-any.whl
+# microscopy_object_detection-1.0.0.tar.gz
+```
+
+## Testing the Package Locally
+
+### Install in Development Mode
+
+```bash
+pip install -e .
+```
+
+### Install from Built Package
+
+```bash
+pip install dist/microscopy_object_detection-1.0.0-py3-none-any.whl
+```
+
+### Test the Installation
+
+```bash
+# Test CLI
+microscopy-detect --version
+
+# Test GUI launcher
+microscopy-detect-gui
+```
+
+## Publishing to PyPI
+
+### 1. Configure PyPI Credentials
+
+Create or update `~/.pypirc`:
+
+```ini
+[pypi]
+username = __token__
+password = pypi-YOUR-API-TOKEN-HERE
+```
+
+### 2. Upload to Test PyPI (Recommended First)
+
+```bash
+python -m twine upload --repository testpypi dist/*
+```
+
+Then test installation:
+
+```bash
+pip install --index-url https://test.pypi.org/simple/ microscopy-object-detection
+```
+
+### 3. Upload to PyPI
+
+```bash
+python -m twine upload dist/*
+```
+
+## Version Management
+
+Update version in multiple files:
+- `setup.py`: Update `version` parameter
+- `pyproject.toml`: Update `version` field
+- `src/__init__.py`: Update `__version__` variable
+
+## Git Tags
+
+After publishing, tag the release:
+
+```bash
+git tag -a v1.0.0 -m "Release version 1.0.0"
+git push origin v1.0.0
+```
+
+## Package Structure
+
+The built package includes:
+- All Python source files in `src/`
+- Configuration files in `config/`
+- Database schema file (`src/database/schema.sql`)
+- Documentation files (README.md, LICENSE, etc.)
+- Entry points for CLI and GUI
+
+## Troubleshooting
+
+### Import Errors
+If you get import errors, ensure:
+- All `__init__.py` files are present
+- Package structure follows the setup configuration
+- Dependencies are listed in `requirements.txt`
+
+### Missing Files
+If files are missing in the built package:
+- Check `MANIFEST.in` includes the required patterns
+- Check `pyproject.toml` package-data configuration
+- Rebuild with `python -m build --no-isolation` for debugging
+
+### Version Conflicts
+If version conflicts occur:
+- Ensure version is consistent across all files
+- Clear build artifacts and rebuild
+- Check for cached installations: `pip list | grep microscopy`
+
+## CI/CD Integration
+
+### GitHub Actions Example
+
+```yaml
+name: Build and Publish
+
+on:
+  release:
+    types: [created]
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+      with:
+        python-version: '3.8'
+    - name: Install dependencies
+      run: |
+        pip install build twine
+    - name: Build package
+      run: python -m build
+    - name: Publish to PyPI
+      env:
+        TWINE_USERNAME: __token__
+        TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
+      run: twine upload dist/*
+```
+
+## Best Practices
+
+1. **Version Bumping**: Use semantic versioning (MAJOR.MINOR.PATCH)
+2. **Testing**: Always test on Test PyPI before publishing to PyPI
+3. **Documentation**: Update README.md and CHANGELOG.md for each release
+4. **Git Tags**: Tag releases in git for easy reference
+5. **Dependencies**: Keep requirements.txt updated and specify version ranges
+
+## Resources
+
+- [Python Packaging Guide](https://packaging.python.org/)
+- [setuptools Documentation](https://setuptools.pypa.io/)
+- [PyPI Publishing Guide](https://packaging.python.org/tutorials/packaging-projects/)
--- a/INSTALL_TEST.md
+++ b/INSTALL_TEST.md
@@ -0,0 +1,236 @@
+# Installation Testing Guide
+
+This guide helps you verify that the package installation works correctly.
+
+## Clean Installation Test
+
+### 1. Remove Any Previous Installations
+
+```bash
+# Deactivate any active virtual environment
+deactivate
+
+# Remove old virtual environment (if exists)
+rm -rf venv
+
+# Create fresh virtual environment
+python3 -m venv venv
+source venv/bin/activate  # On Linux/Mac
+# or
+venv\Scripts\activate  # On Windows
+```
+
+### 2. Install the Package
+
+#### Option A: Editable/Development Install
+
+```bash
+pip install -e .
+```
+
+This allows you to modify source code and see changes immediately.
+
+#### Option B: Regular Install
+
+```bash
+pip install .
+```
+
+This installs the package as if it were from PyPI.
+
+### 3. Verify Installation
+
+```bash
+# Check package is installed
+pip list | grep microscopy
+
+# Check version
+microscopy-detect --version
+# Expected output: microscopy-object-detection 1.0.0
+
+# Test Python import
+python -c "import src; print(src.__version__)"
+# Expected output: 1.0.0
+```
+
+### 4. Test Entry Points
+
+```bash
+# Test CLI
+microscopy-detect --help
+
+# Test GUI launcher (will open window)
+microscopy-detect-gui
+```
+
+### 5. Verify Package Contents
+
+```python
+# Run this in Python shell
+import src
+import src.database
+import src.model
+import src.gui
+
+# Check schema file is included
+from pathlib import Path
+import src.database
+db_path = Path(src.database.__file__).parent
+schema_file = db_path / 'schema.sql'
+print(f"Schema file exists: {schema_file.exists()}")
+# Expected: Schema file exists: True
+```
+
+## Troubleshooting
+
+### Issue: ModuleNotFoundError
+
+**Error:**
+```
+ModuleNotFoundError: No module named 'src'
+```
+
+**Solution:**
+```bash
+# Reinstall with verbose output
+pip install -e . -v
+
+# Or try regular install
+pip install . --force-reinstall
+```
+
+### Issue: Entry Points Not Working
+
+**Error:**
+```
+microscopy-detect: command not found
+```
+
+**Solution:**
+```bash
+# Check if scripts are in PATH
+which microscopy-detect
+
+# If not found, check pip install location
+pip show microscopy-object-detection
+
+# You might need to add to PATH or use full path
+~/.local/bin/microscopy-detect  # Linux
+```
+
+### Issue: Import Errors for PySide6
+
+**Error:**
+```
+ImportError: cannot import name 'QApplication' from 'PySide6.QtWidgets'
+```
+
+**Solution:**
+```bash
+# Install Qt dependencies (Linux only)
+sudo apt-get install libxcb-xinerama0
+
+# Reinstall PySide6
+pip uninstall PySide6
+pip install PySide6
+```
+
+### Issue: Config Files Not Found
+
+**Error:**
+```
+FileNotFoundError: config/app_config.yaml
+```
+
+**Solution:**
+The config file should be created automatically. If not:
+```bash
+# Create config directory in your home
+mkdir -p ~/.microscopy-detect
+cp config/app_config.yaml ~/.microscopy-detect/
+
+# Or run from source directory first time
+cd /home/martin/code/object_detection
+python main.py
+```
+
+## Manual Testing Checklist
+
+- [ ] Package installs without errors
+- [ ] Version command works (`microscopy-detect --version`)
+- [ ] Help command works (`microscopy-detect --help`)
+- [ ] GUI launches (`microscopy-detect-gui`)
+- [ ] Can import all modules in Python
+- [ ] Database schema file is accessible
+- [ ] Configuration loads correctly
+
+## Build and Install from Wheel
+
+```bash
+# Build the package
+python -m build
+
+# Install from wheel
+pip install dist/microscopy_object_detection-1.0.0-py3-none-any.whl
+
+# Test
+microscopy-detect --version
+```
+
+## Uninstall
+
+```bash
+pip uninstall microscopy-object-detection
+```
+
+## Development Workflow
+
+### After Code Changes
+
+If installed with `-e` (editable mode):
+- Python code changes are immediately available
+- No need to reinstall
+
+If installed with regular `pip install .`:
+- Reinstall after changes: `pip install . --force-reinstall`
+
+### After Adding New Files
+
+```bash
+# Reinstall to include new files
+pip install -e . --force-reinstall
+```
+
+## Expected Installation Output
+
+```
+Processing /home/martin/code/object_detection
+  Installing build dependencies ... done
+  Getting requirements to build wheel ... done
+  Preparing metadata (pyproject.toml) ... done
+Building wheels for collected packages: microscopy-object-detection
+  Building wheel for microscopy-object-detection (pyproject.toml) ... done
+Successfully built microscopy-object-detection
+Installing collected packages: microscopy-object-detection
+Successfully installed microscopy-object-detection-1.0.0
+```
+
+## Success Criteria
+
+Installation is successful when:
+1. ✅ No error messages during installation
+2. ✅ `pip list` shows the package
+3. ✅ `microscopy-detect --version` returns correct version
+4. ✅ GUI launches without errors
+5. ✅ All Python modules can be imported
+6. ✅ Database operations work
+7. ✅ Detection functionality works
+
+## Next Steps After Successful Install
+
+1. Configure image repository path
+2. Run first detection
+3. Train a custom model
+4. Export results
+
+For usage instructions, see [QUICKSTART.md](QUICKSTART.md)
--- a/21
+++ b/21
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Your Name
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -0,0 +1,37 @@
+# Include documentation files
+include README.md
+include LICENSE
+include ARCHITECTURE.md
+include IMPLEMENTATION_GUIDE.md
+include QUICKSTART.md
+include PLAN_SUMMARY.md
+
+# Include requirements
+include requirements.txt
+
+# Include configuration files
+recursive-include config *.yaml
+recursive-include config *.yml
+
+# Include database schema
+recursive-include src/database *.sql
+
+# Include tests
+recursive-include tests *.py
+
+# Exclude compiled Python files
+global-exclude *.pyc
+global-exclude *.pyo
+global-exclude __pycache__
+global-exclude *.so
+global-exclude .DS_Store
+
+# Exclude git and IDE files
+global-exclude .git*
+global-exclude .vscode
+global-exclude .idea
+
+# Exclude build artifacts
+prune build
+prune dist
+prune *.egg-info
--- a/QUICKSTART.md
+++ b/QUICKSTART.md
@@ -38,7 +38,7 @@ This will install:
 - OpenCV and Pillow (image processing)
 - And other dependencies

-**Note:** The first run will automatically download the YOLOv8s.pt model (~22MB).
+**Note:** The first run will automatically download the YOLOv8s-seg.pt segmentation model (~23MB).

 ### 4. Verify Installation

@@ -84,11 +84,11 @@ In the Settings dialog:
 ### Single Image Detection

 1. Go to the **Detection** tab
-2. Select a model from the dropdown (default: Base Model yolov8s.pt)
+2. Select a model from the dropdown (default: Base Model yolov8s-seg.pt)
 3. Adjust confidence threshold with the slider
 4. Click "Detect Single Image"
 5. Select an image file
-6. View results in the results panel
+6. View results with segmentation masks overlaid on the image

 ### Batch Detection

@@ -108,9 +108,18 @@ Detection results include:
 - **Class names**: Types of objects detected (e.g., organelle, membrane_branch)
 - **Confidence scores**: Detection confidence (0-1)
 - **Bounding boxes**: Object locations (stored in database)
+- **Segmentation masks**: Pixel-accurate polygon coordinates for each detected object

 All results are stored in the SQLite database at [`data/detections.db`](data/detections.db).

+### Segmentation Visualization
+
+The application automatically displays segmentation masks when available:
+- Semi-transparent colored overlay (30% opacity) showing the exact shape of detected objects
+- Polygon contours outlining each segmentation
+- Color-coded by object class
+- Toggle-able in future versions
+
 ## Database

 The application uses SQLite to store:
@@ -176,7 +185,7 @@ sudo apt-get install libxcb-xinerama0
 ### Detection Not Working

 **No models available**
- The base YOLOv8s model will be downloaded automatically on first use
+- The base YOLOv8s-seg segmentation model will be downloaded automatically on first use
 - Make sure you have internet connection for the first run

 **Images not found**
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # Microscopy Object Detection Application

-A desktop application for detecting organelles and membrane branching structures in microscopy images using YOLOv8, featuring comprehensive training, validation, and visualization capabilities.
+A desktop application for detecting and segmenting organelles and membrane branching structures in microscopy images using YOLOv8-seg, featuring comprehensive training, validation, and visualization capabilities with pixel-accurate segmentation masks.

 ![Python](https://img.shields.io/badge/python-3.8+-blue.svg)
 ![PySide6](https://img.shields.io/badge/PySide6-6.5+-green.svg)
@@ -8,8 +8,8 @@ A desktop application for detecting organelles and membrane branching structures

 ## Features

- **🎯 Object Detection**: Real-time and batch detection of microscopy objects
- **🎓 Model Training**: Fine-tune YOLOv8s on custom microscopy datasets
+- **🎯 Object Detection & Segmentation**: Real-time and batch detection with pixel-accurate segmentation masks
+- **🎓 Model Training**: Fine-tune YOLOv8s-seg on custom microscopy datasets
 - **📊 Validation & Metrics**: Comprehensive model validation with visualization
 - **💾 Database Storage**: SQLite database for detection results and metadata
 - **📈 Visualization**: Interactive plots and charts using pyqtgraph
@@ -34,14 +34,24 @@ A desktop application for detecting organelles and membrane branching structures

 ## Installation

-### 1. Clone the Repository
+### Option 1: Install from PyPI (Recommended)
+
+```bash
+pip install microscopy-object-detection
+```
+
+This will install the package and all its dependencies.
+
+### Option 2: Install from Source
+
+#### 1. Clone the Repository

 ```bash
 git clone <repository-url>
 cd object_detection
 ```

-### 2. Create Virtual Environment
+#### 2. Create Virtual Environment

 ```bash
 # Linux/Mac
@@ -53,25 +63,44 @@ python -m venv venv
 venv\Scripts\activate
 ```

-### 3. Install Dependencies
+#### 3. Install in Development Mode

 ```bash
-pip install -r requirements.txt
+# Install in editable mode with dev dependencies
+pip install -e ".[dev]"
+
+# Or install just the package
+pip install .
 ```

 ### 4. Download Base Model

-The application will automatically download the YOLOv8s.pt model on first use, or you can download it manually:
+The application will automatically download the YOLOv8s-seg.pt segmentation model on first use, or you can download it manually:

 ```bash
 # The model will be downloaded automatically by ultralytics
 # Or download manually from: https://github.com/ultralytics/assets/releases
 ```

+**Note:** YOLOv8s-seg is a segmentation model that provides pixel-accurate masks for detected objects, enabling more precise analysis than standard bounding box detection.
+
 ## Quick Start

 ### 1. Launch the Application

+After installation, you can launch the application in two ways:
+
+**Using the GUI launcher:**
+```bash
+microscopy-detect-gui
+```
+
+**Or using Python directly:**
+```bash
+python -m microscopy_object_detection
+```
+
+**If installed from source:**
 ```bash
 python main.py
 ```
@@ -85,11 +114,12 @@ python main.py
 ### 3. Perform Detection

 1. Navigate to the **Detection** tab
-2. Select a model (default: yolov8s.pt)
+2. Select a model (default: yolov8s-seg.pt)
 3. Choose an image or folder
 4. Set confidence threshold
 5. Click **Detect**
-6. View results and save to database
+6. View results with segmentation masks overlaid
+7. Save results to database

 ### 4. Train Custom Model

@@ -212,8 +242,8 @@ The application uses SQLite with the following main tables:

 - **models**: Stores trained model information and metrics
 - **images**: Stores image metadata and paths
- **detections**: Stores detection results with bounding boxes
- **annotations**: Stores manual annotations (future feature)
+- **detections**: Stores detection results with bounding boxes and segmentation masks (polygon coordinates)
+- **annotations**: Stores manual annotations with optional segmentation masks (future feature)

 See [`ARCHITECTURE.md`](ARCHITECTURE.md) for detailed schema information.

@@ -230,7 +260,7 @@ image_repository:
  allowed_extensions: [".jpg", ".jpeg", ".png", ".tif", ".tiff"]

 models:
-  default_base_model: "yolov8s.pt"
+  default_base_model: "yolov8s-seg.pt"
  models_directory: "data/models"

 training:
@@ -258,7 +288,7 @@ visualization:
 from src.model.yolo_wrapper import YOLOWrapper

 # Initialize wrapper
-yolo = YOLOWrapper("yolov8s.pt")
+yolo = YOLOWrapper("yolov8s-seg.pt")

 # Train model
 results = yolo.train(
@@ -393,10 +423,10 @@ make html

 **Issue**: Model not found error

-**Solution**: Ensure YOLOv8s.pt is downloaded. Run:
+**Solution**: Ensure YOLOv8s-seg.pt is downloaded. Run:
 ```python
 from ultralytics import YOLO
-model = YOLO('yolov8s.pt')  # Will auto-download
+model = YOLO('yolov8s-seg.pt')  # Will auto-download
 ```


--- a/config/app_config.yaml
+++ b/config/app_config.yaml
@@ -10,7 +10,7 @@ image_repository:
  - .tiff
  - .bmp
 models:
-  default_base_model: yolov8s.pt
+  default_base_model: yolov8s-seg.pt
  models_directory: data/models
 training:
  default_epochs: 100
--- a/docs/IMAGE_CLASS_USAGE.md
+++ b/docs/IMAGE_CLASS_USAGE.md
@@ -0,0 +1,220 @@
+# Image Class Usage Guide
+
+The `Image` class provides a convenient way to load and work with images in the microscopy object detection application.
+
+## Supported Formats
+
+The Image class supports the following image formats:
+- `.jpg`, `.jpeg` - JPEG images
+- `.png` - PNG images
+- `.tif`, `.tiff` - TIFF images (commonly used in microscopy)
+- `.bmp` - Bitmap images
+
+## Basic Usage
+
+### Loading an Image
+
+```python
+from src.utils import Image, ImageLoadError
+
+# Load an image from a file path
+try:
+    img = Image("path/to/image.jpg")
+    print(f"Loaded image: {img.width}x{img.height} pixels")
+except ImageLoadError as e:
+    print(f"Failed to load image: {e}")
+```
+
+### Accessing Image Properties
+
+```python
+img = Image("microscopy_image.tif")
+
+# Basic properties
+print(f"Width: {img.width} pixels")
+print(f"Height: {img.height} pixels")
+print(f"Channels: {img.channels}")
+print(f"Format: {img.format}")
+print(f"Shape: {img.shape}")  # (height, width, channels)
+
+# File information
+print(f"File size: {img.size_mb:.2f} MB")
+print(f"File size: {img.size_bytes} bytes")
+
+# Image type checks
+print(f"Is color: {img.is_color()}")
+print(f"Is grayscale: {img.is_grayscale()}")
+
+# String representation
+print(img)  # Shows summary of image properties
+```
+
+### Working with Image Data
+
+```python
+import numpy as np
+
+img = Image("sample.png")
+
+# Get image data as numpy array (OpenCV format, BGR)
+bgr_data = img.data
+print(f"Data shape: {bgr_data.shape}")
+print(f"Data type: {bgr_data.dtype}")
+
+# Get image as RGB (for display or processing)
+rgb_data = img.get_rgb()
+
+# Get grayscale version
+gray_data = img.get_grayscale()
+
+# Create a copy (for modifications)
+img_copy = img.copy()
+img_copy[0, 0] = [255, 255, 255]  # Modify copy, original unchanged
+
+# Resize image (returns new array, doesn't modify original)
+resized = img.resize(640, 640)
+```
+
+### Using PIL Image
+
+```python
+img = Image("photo.jpg")
+
+# Access as PIL Image (RGB format)
+pil_img = img.pil_image
+
+# Use PIL methods
+pil_img.show()  # Display image
+pil_img.save("output.png")  # Save with PIL
+```
+
+## Integration with YOLO
+
+```python
+from src.utils import Image
+from ultralytics import YOLO
+
+# Load model and image
+model = YOLO("yolov8n.pt")
+img = Image("microscopy/cell_01.tif")
+
+# Run inference (YOLO accepts file paths or numpy arrays)
+results = model(img.data)
+
+# Or use the file path directly
+results = model(str(img.path))
+```
+
+## Error Handling
+
+```python
+from src.utils import Image, ImageLoadError
+
+def process_image(image_path):
+    try:
+        img = Image(image_path)
+        # Process the image...
+        return img
+    except ImageLoadError as e:
+        print(f"Cannot load image: {e}")
+        return None
+```
+
+## Advanced Usage
+
+### Batch Processing
+
+```python
+from pathlib import Path
+from src.utils import Image, ImageLoadError
+
+def process_image_directory(directory):
+    """Process all images in a directory."""
+    image_paths = Path(directory).glob("*.tif")
+    
+    for path in image_paths:
+        try:
+            img = Image(path)
+            print(f"Processing {img.path.name}: {img.width}x{img.height}")
+            # Process the image...
+        except ImageLoadError as e:
+            print(f"Skipping {path}: {e}")
+```
+
+### Using with OpenCV Operations
+
+```python
+import cv2
+from src.utils import Image
+
+img = Image("input.jpg")
+
+# Apply OpenCV operations on the data
+blurred = cv2.GaussianBlur(img.data, (5, 5), 0)
+edges = cv2.Canny(img.data, 100, 200)
+
+# Note: These operations don't modify the original img.data
+```
+
+### Memory Efficient Processing
+
+```python
+from src.utils import Image
+
+# The Image class loads data into memory
+img = Image("large_image.tif")
+print(f"Image size in memory: {img.data.nbytes / (1024**2):.2f} MB")
+
+# When processing many images, consider loading one at a time
+# and releasing memory by deleting the object
+del img
+```
+
+## Best Practices
+
+1. **Always use try-except** when loading images to handle errors gracefully
+2. **Check image properties** before processing to ensure compatibility
+3. **Use copy()** when you need to modify image data without affecting the original
+4. **Path objects work too** - The class accepts both strings and Path objects
+5. **Consider memory usage** when working with large images or batches
+
+## Example: Complete Workflow
+
+```python
+from src.utils import Image, ImageLoadError
+from src.utils.file_utils import get_image_files
+
+def analyze_microscopy_images(directory):
+    """Analyze all microscopy images in a directory."""
+    
+    # Get all image files
+    image_files = get_image_files(directory, recursive=True)
+    
+    results = []
+    for image_path in image_files:
+        try:
+            # Load image
+            img = Image(image_path)
+            
+            # Analyze
+            result = {
+                'filename': img.path.name,
+                'width': img.width,
+                'height': img.height,
+                'channels': img.channels,
+                'format': img.format,
+                'size_mb': img.size_mb,
+                'is_color': img.is_color()
+            }
+            
+            results.append(result)
+            print(f"✓ Analyzed {img.path.name}")
+            
+        except ImageLoadError as e:
+            print(f"✗ Failed to load {image_path}: {e}")
+    
+    return results
+
+# Run analysis
+results = analyze_microscopy_images("data/datasets/cells")
+print(f"\nProcessed {len(results)} images")
--- a/examples/image_demo.py
+++ b/examples/image_demo.py
@@ -0,0 +1,151 @@
+"""
+Example script demonstrating the Image class functionality.
+"""
+
+import sys
+from pathlib import Path
+
+# Add parent directory to path for imports
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from src.utils import Image, ImageLoadError
+
+
+def demonstrate_image_loading():
+    """Demonstrate basic image loading functionality."""
+    print("=" * 60)
+    print("Image Class Demonstration")
+    print("=" * 60)
+
+    # Example 1: Try to load an image (replace with your own path)
+    example_paths = [
+        "data/datasets/example.jpg",
+        "data/datasets/sample.png",
+        "tests/test_image.jpg",
+    ]
+
+    loaded_img = None
+    for image_path in example_paths:
+        if Path(image_path).exists():
+            try:
+                print(f"\n1. Loading image: {image_path}")
+                img = Image(image_path)
+                loaded_img = img
+                print(f"   ✓ Successfully loaded!")
+                print(f"   {img}")
+                break
+            except ImageLoadError as e:
+                print(f"   ✗ Failed: {e}")
+        else:
+            print(f"\n1. Image not found: {image_path}")
+
+    if loaded_img is None:
+        print("\nNo example images found. Creating a test image...")
+        create_test_image()
+        return
+
+    # Example 2: Access image properties
+    print(f"\n2. Image Properties:")
+    print(f"   Width: {loaded_img.width} pixels")
+    print(f"   Height: {loaded_img.height} pixels")
+    print(f"   Channels: {loaded_img.channels}")
+    print(f"   Format: {loaded_img.format.upper()}")
+    print(f"   Shape: {loaded_img.shape}")
+    print(f"   File size: {loaded_img.size_mb:.2f} MB")
+    print(f"   Is color: {loaded_img.is_color()}")
+    print(f"   Is grayscale: {loaded_img.is_grayscale()}")
+
+    # Example 3: Get different formats
+    print(f"\n3. Accessing Image Data:")
+    print(f"   BGR data shape: {loaded_img.data.shape}")
+    print(f"   RGB data shape: {loaded_img.get_rgb().shape}")
+    print(f"   Grayscale shape: {loaded_img.get_grayscale().shape}")
+    print(f"   PIL image mode: {loaded_img.pil_image.mode}")
+
+    # Example 4: Resizing
+    print(f"\n4. Resizing Image:")
+    resized = loaded_img.resize(320, 320)
+    print(f"   Original size: {loaded_img.width}x{loaded_img.height}")
+    print(f"   Resized to: {resized.shape[1]}x{resized.shape[0]}")
+
+    # Example 5: Working with copies
+    print(f"\n5. Creating Copies:")
+    copy = loaded_img.copy()
+    print(f"   Created copy with shape: {copy.shape}")
+    print(f"   Original data unchanged: {(loaded_img.data == copy).all()}")
+
+    print("\n" + "=" * 60)
+    print("Demonstration Complete!")
+    print("=" * 60)
+
+
+def create_test_image():
+    """Create a test image for demonstration purposes."""
+    import cv2
+    import numpy as np
+
+    print("\nCreating a test image...")
+
+    # Create a colorful test image
+    width, height = 400, 300
+    test_img = np.zeros((height, width, 3), dtype=np.uint8)
+
+    # Add some colors
+    test_img[:100, :] = [255, 0, 0]  # Blue section
+    test_img[100:200, :] = [0, 255, 0]  # Green section
+    test_img[200:, :] = [0, 0, 255]  # Red section
+
+    # Save the test image
+    test_path = Path("test_demo_image.png")
+    cv2.imwrite(str(test_path), test_img)
+    print(f"Test image created: {test_path}")
+
+    # Now load and demonstrate with it
+    try:
+        img = Image(test_path)
+        print(f"\nLoaded test image: {img}")
+        print(f"Dimensions: {img.width}x{img.height}")
+        print(f"Channels: {img.channels}")
+        print(f"Format: {img.format}")
+
+        # Clean up
+        test_path.unlink()
+        print(f"\nTest image cleaned up.")
+
+    except ImageLoadError as e:
+        print(f"Error loading test image: {e}")
+
+
+def demonstrate_error_handling():
+    """Demonstrate error handling."""
+    print("\n" + "=" * 60)
+    print("Error Handling Demonstration")
+    print("=" * 60)
+
+    # Try to load non-existent file
+    print("\n1. Loading non-existent file:")
+    try:
+        img = Image("nonexistent.jpg")
+    except ImageLoadError as e:
+        print(f"   ✓ Caught error: {e}")
+
+    # Try unsupported format
+    print("\n2. Loading unsupported format:")
+    try:
+        # Create a text file
+        test_file = Path("test.txt")
+        test_file.write_text("not an image")
+        img = Image(test_file)
+    except ImageLoadError as e:
+        print(f"   ✓ Caught error: {e}")
+        test_file.unlink()  # Clean up
+
+    print("\n" + "=" * 60)
+
+
+if __name__ == "__main__":
+    print("\n")
+    demonstrate_image_loading()
+    print("\n")
+    demonstrate_error_handling()
+    print("\n")
--- a/main.py
+++ b/main.py
@@ -6,12 +6,13 @@ Main entry point for the application.
 import sys
 from pathlib import Path

-# Add src directory to path
+# Add src directory to path for development mode
 sys.path.insert(0, str(Path(__file__).parent))

 from PySide6.QtWidgets import QApplication
 from PySide6.QtCore import Qt

+from src import __version__
 from src.gui.main_window import MainWindow
 from src.utils.logger import setup_logging
 from src.utils.config_manager import ConfigManager
@@ -37,7 +38,7 @@ def main():
    app = QApplication(sys.argv)
    app.setApplicationName("Microscopy Object Detection")
    app.setOrganizationName("MicroscopyLab")
-    app.setApplicationVersion("1.0.0")
+    app.setApplicationVersion(__version__)

    # Set application style
    app.setStyle("Fusion")
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -0,0 +1,102 @@
+[build-system]
+requires = ["setuptools>=45", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "microscopy-object-detection"
+version = "1.0.0"
+description = "Desktop application for detecting and segmenting organelles in microscopy images using YOLOv8-seg"
+readme = "README.md"
+requires-python = ">=3.8"
+license = { text = "MIT" }
+authors = [{ name = "Your Name", email = "your.email@example.com" }]
+keywords = [
+    "microscopy",
+    "yolov8",
+    "object-detection",
+    "segmentation",
+    "computer-vision",
+    "deep-learning",
+]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Science/Research",
+    "Topic :: Scientific/Engineering :: Image Recognition",
+    "Topic :: Scientific/Engineering :: Bio-Informatics",
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Operating System :: OS Independent",
+]
+
+dependencies = [
+    "ultralytics>=8.0.0",
+    "PySide6>=6.5.0",
+    "pyqtgraph>=0.13.0",
+    "numpy>=1.24.0",
+    "opencv-python>=4.8.0",
+    "Pillow>=10.0.0",
+    "PyYAML>=6.0",
+    "pandas>=2.0.0",
+    "openpyxl>=3.1.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=7.0.0",
+    "pytest-cov>=4.0.0",
+    "black>=23.0.0",
+    "pylint>=2.17.0",
+    "mypy>=1.0.0",
+]
+
+[project.urls]
+Homepage = "https://github.com/yourusername/object_detection"
+Documentation = "https://github.com/yourusername/object_detection/blob/main/README.md"
+Repository = "https://github.com/yourusername/object_detection"
+"Bug Tracker" = "https://github.com/yourusername/object_detection/issues"
+
+[project.scripts]
+microscopy-detect = "src.cli:main"
+
+[project.gui-scripts]
+microscopy-detect-gui = "src.gui_launcher:main"
+
+[tool.setuptools]
+packages = [
+    "src",
+    "src.database",
+    "src.model",
+    "src.gui",
+    "src.gui.tabs",
+    "src.gui.dialogs",
+    "src.gui.widgets",
+    "src.utils",
+]
+include-package-data = true
+
+[tool.setuptools.package-data]
+"src.database" = ["*.sql"]
+
+[tool.black]
+line-length = 88
+target-version = ['py38', 'py39', 'py310', 'py311']
+include = '\.pyi?$'
+
+[tool.pylint.messages_control]
+max-line-length = 88
+
+[tool.mypy]
+python_version = "3.8"
+warn_return_any = true
+warn_unused_configs = true
+disallow_untyped_defs = false
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = ["test_*.py"]
+python_functions = ["test_*"]
+addopts = "-v --cov=src --cov-report=term-missing"
--- a/setup.py
+++ b/setup.py
@@ -0,0 +1,56 @@
+"""Setup script for Microscopy Object Detection Application."""
+
+from setuptools import setup, find_packages
+from pathlib import Path
+
+# Read the contents of README file
+this_directory = Path(__file__).parent
+long_description = (this_directory / "README.md").read_text(encoding="utf-8")
+
+# Read requirements
+requirements = (this_directory / "requirements.txt").read_text().splitlines()
+requirements = [
+    req.strip() for req in requirements if req.strip() and not req.startswith("#")
+]
+
+setup(
+    name="microscopy-object-detection",
+    version="1.0.0",
+    author="Your Name",
+    author_email="your.email@example.com",
+    description="Desktop application for detecting and segmenting organelles in microscopy images using YOLOv8-seg",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/yourusername/object_detection",
+    packages=find_packages(exclude=["tests", "tests.*", "docs"]),
+    include_package_data=True,
+    install_requires=requirements,
+    python_requires=">=3.8",
+    classifiers=[
+        "Development Status :: 4 - Beta",
+        "Intended Audience :: Science/Research",
+        "Topic :: Scientific/Engineering :: Image Recognition",
+        "Topic :: Scientific/Engineering :: Bio-Informatics",
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
+        "Operating System :: OS Independent",
+    ],
+    entry_points={
+        "console_scripts": [
+            "microscopy-detect=src.cli:main",
+        ],
+        "gui_scripts": [
+            "microscopy-detect-gui=src.gui_launcher:main",
+        ],
+    },
+    keywords="microscopy yolov8 object-detection segmentation computer-vision deep-learning",
+    project_urls={
+        "Bug Reports": "https://github.com/yourusername/object_detection/issues",
+        "Source": "https://github.com/yourusername/object_detection",
+        "Documentation": "https://github.com/yourusername/object_detection/blob/main/README.md",
+    },
+)
--- a/src/init.py
+++ b/src/init.py
@@ -0,0 +1,19 @@
+"""
+Microscopy Object Detection Application
+
+A desktop application for detecting and segmenting organelles and membrane
+branching structures in microscopy images using YOLOv8-seg.
+"""
+
+__version__ = "1.0.0"
+__author__ = "Your Name"
+__email__ = "your.email@example.com"
+__license__ = "MIT"
+
+# Package metadata
+__all__ = [
+    "__version__",
+    "__author__",
+    "__email__",
+    "__license__",
+]
--- a/src/cli.py
+++ b/src/cli.py
@@ -0,0 +1,61 @@
+"""
+Command-line interface for microscopy object detection application.
+"""
+
+import sys
+import argparse
+from pathlib import Path
+
+from src import __version__
+
+
+def main():
+    """Main CLI entry point."""
+    parser = argparse.ArgumentParser(
+        description="Microscopy Object Detection Application - CLI Interface",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Launch GUI
+  microscopy-detect-gui
+  
+  # Show version
+  microscopy-detect --version
+  
+  # Get help
+  microscopy-detect --help
+        """,
+    )
+
+    parser.add_argument(
+        "--version",
+        action="version",
+        version=f"microscopy-object-detection {__version__}",
+    )
+
+    parser.add_argument(
+        "--gui",
+        action="store_true",
+        help="Launch the GUI application (same as microscopy-detect-gui)",
+    )
+
+    args = parser.parse_args()
+
+    if args.gui:
+        # Launch GUI
+        try:
+            from src.gui_launcher import main as gui_main
+
+            gui_main()
+        except Exception as e:
+            print(f"Error launching GUI: {e}", file=sys.stderr)
+            sys.exit(1)
+    else:
+        # Show help if no arguments provided
+        parser.print_help()
+        print("\nTo launch the GUI, use: microscopy-detect-gui")
+        return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
--- a/src/database/db_manager.py
+++ b/src/database/db_manager.py
@@ -6,7 +6,7 @@ Handles all database operations including CRUD operations, queries, and exports.
 import sqlite3
 import json
 from datetime import datetime
-from typing import List, Dict, Optional, Tuple, Any
+from typing import List, Dict, Optional, Tuple, Any, Union
 from pathlib import Path
 import csv
 import hashlib
@@ -30,18 +30,48 @@ class DatabaseManager:
        # Create directory if it doesn't exist
        Path(self.db_path).parent.mkdir(parents=True, exist_ok=True)

-        # Read schema file and execute
-        schema_path = Path(__file__).parent / "schema.sql"
-        with open(schema_path, "r") as f:
-            schema_sql = f.read()
-
        conn = self.get_connection()
        try:
+            # Check if annotations table needs migration
+            self._migrate_annotations_table(conn)
+
+            # Read schema file and execute
+            schema_path = Path(__file__).parent / "schema.sql"
+            with open(schema_path, "r") as f:
+                schema_sql = f.read()
+
            conn.executescript(schema_sql)
            conn.commit()
        finally:
            conn.close()

+    def _migrate_annotations_table(self, conn: sqlite3.Connection) -> None:
+        """
+        Migrate annotations table from old schema (class_name) to new schema (class_id).
+        """
+        cursor = conn.cursor()
+
+        # Check if annotations table exists
+        cursor.execute(
+            "SELECT name FROM sqlite_master WHERE type='table' AND name='annotations'"
+        )
+        if not cursor.fetchone():
+            # Table doesn't exist yet, no migration needed
+            return
+
+        # Check if table has old schema (class_name column)
+        cursor.execute("PRAGMA table_info(annotations)")
+        columns = {row[1]: row for row in cursor.fetchall()}
+
+        if "class_name" in columns and "class_id" not in columns:
+            # Old schema detected, need to migrate
+            print("Migrating annotations table to new schema with class_id...")
+
+            # Drop old annotations table (assuming no critical data since this is a new feature)
+            cursor.execute("DROP TABLE IF EXISTS annotations")
+            conn.commit()
+            print("Old annotations table dropped, will be recreated with new schema")
+
    def get_connection(self) -> sqlite3.Connection:
        """Get database connection with proper settings."""
        conn = sqlite3.connect(self.db_path)
@@ -56,7 +86,7 @@ class DatabaseManager:
        model_name: str,
        model_version: str,
        model_path: str,
-        base_model: str = "yolov8s.pt",
+        base_model: str = "yolov8s-seg.pt",
        training_params: Optional[Dict] = None,
        metrics: Optional[Dict] = None,
    ) -> int:
@@ -243,6 +273,7 @@ class DatabaseManager:
        class_name: str,
        bbox: Tuple[float, float, float, float],  # (x_min, y_min, x_max, y_max)
        confidence: float,
+        segmentation_mask: Optional[List[List[float]]] = None,
        metadata: Optional[Dict] = None,
    ) -> int:
        """
@@ -254,6 +285,7 @@ class DatabaseManager:
            class_name: Detected object class
            bbox: Bounding box coordinates (normalized 0-1)
            confidence: Detection confidence score
+            segmentation_mask: Polygon coordinates for segmentation [[x1,y1], [x2,y2], ...]
            metadata: Additional metadata

        Returns:
@@ -265,8 +297,8 @@ class DatabaseManager:
            x_min, y_min, x_max, y_max = bbox
            cursor.execute(
                """
-                INSERT INTO detections (image_id, model_id, class_name, x_min, y_min, x_max, y_max, confidence, metadata)
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
+                INSERT INTO detections (image_id, model_id, class_name, x_min, y_min, x_max, y_max, confidence, segmentation_mask, metadata)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                """,
                (
                    image_id,
@@ -277,6 +309,7 @@ class DatabaseManager:
                    x_max,
                    y_max,
                    confidence,
+                    json.dumps(segmentation_mask) if segmentation_mask else None,
                    json.dumps(metadata) if metadata else None,
                ),
            )
@@ -302,8 +335,8 @@ class DatabaseManager:
                bbox = det["bbox"]
                cursor.execute(
                    """
-                    INSERT INTO detections (image_id, model_id, class_name, x_min, y_min, x_max, y_max, confidence, metadata)
-                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
+                    INSERT INTO detections (image_id, model_id, class_name, x_min, y_min, x_max, y_max, confidence, segmentation_mask, metadata)
+                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                    """,
                    (
                        det["image_id"],
@@ -314,6 +347,11 @@ class DatabaseManager:
                        bbox[2],
                        bbox[3],
                        det["confidence"],
+                        (
+                            json.dumps(det.get("segmentation_mask"))
+                            if det.get("segmentation_mask")
+                            else None
+                        ),
                        (
                            json.dumps(det.get("metadata"))
                            if det.get("metadata")
@@ -385,9 +423,11 @@ class DatabaseManager:
            detections = []
            for row in cursor.fetchall():
                det = dict(row)
-                # Parse JSON metadata
+                # Parse JSON fields
                if det.get("metadata"):
                    det["metadata"] = json.loads(det["metadata"])
+                if det.get("segmentation_mask"):
+                    det["segmentation_mask"] = json.loads(det["segmentation_mask"])
                detections.append(det)

            return detections
@@ -538,6 +578,7 @@ class DatabaseManager:
                    "x_max",
                    "y_max",
                    "confidence",
+                    "segmentation_mask",
                    "detected_at",
                ]
                writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
@@ -545,6 +586,11 @@ class DatabaseManager:

                for det in detections:
                    row = {k: det[k] for k in fieldnames if k in det}
+                    # Convert segmentation mask list to JSON string for CSV
+                    if row.get("segmentation_mask") and isinstance(
+                        row["segmentation_mask"], list
+                    ):
+                        row["segmentation_mask"] = json.dumps(row["segmentation_mask"])
                    writer.writerow(row)

            return True
@@ -577,22 +623,46 @@ class DatabaseManager:
    def add_annotation(
        self,
        image_id: int,
-        class_name: str,
+        class_id: int,
        bbox: Tuple[float, float, float, float],
        annotator: str,
+        segmentation_mask: Optional[List[List[float]]] = None,
        verified: bool = False,
    ) -> int:
-        """Add manual annotation."""
+        """
+        Add manual annotation.
+
+        Args:
+            image_id: ID of the image
+            class_id: ID of the object class (foreign key to object_classes)
+            bbox: Bounding box coordinates (normalized 0-1)
+            annotator: Name of person/tool creating annotation
+            segmentation_mask: Polygon coordinates for segmentation
+            verified: Whether annotation has been verified
+
+        Returns:
+            ID of the inserted annotation
+        """
        conn = self.get_connection()
        try:
            cursor = conn.cursor()
            x_min, y_min, x_max, y_max = bbox
            cursor.execute(
                """
-                INSERT INTO annotations (image_id, class_name, x_min, y_min, x_max, y_max, annotator, verified)
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                INSERT INTO annotations (image_id, class_id, x_min, y_min, x_max, y_max, segmentation_mask, annotator, verified)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
                """,
-                (image_id, class_name, x_min, y_min, x_max, y_max, annotator, verified),
+                (
+                    image_id,
+                    class_id,
+                    x_min,
+                    y_min,
+                    x_max,
+                    y_max,
+                    json.dumps(segmentation_mask) if segmentation_mask else None,
+                    annotator,
+                    verified,
+                ),
            )
            conn.commit()
            return cursor.lastrowid
@@ -600,15 +670,197 @@ class DatabaseManager:
            conn.close()

    def get_annotations_for_image(self, image_id: int) -> List[Dict]:
-        """Get all annotations for an image."""
+        """
+        Get all annotations for an image with class information.
+
+        Args:
+            image_id: ID of the image
+
+        Returns:
+            List of annotation dictionaries with joined class information
+        """
        conn = self.get_connection()
        try:
            cursor = conn.cursor()
-            cursor.execute("SELECT * FROM annotations WHERE image_id = ?", (image_id,))
+            cursor.execute(
+                """
+                SELECT
+                    a.*,
+                    c.class_name,
+                    c.color as class_color,
+                    c.description as class_description
+                FROM annotations a
+                JOIN object_classes c ON a.class_id = c.id
+                WHERE a.image_id = ?
+                ORDER BY a.created_at DESC
+                """,
+                (image_id,),
+            )
+            annotations = []
+            for row in cursor.fetchall():
+                ann = dict(row)
+                if ann.get("segmentation_mask"):
+                    ann["segmentation_mask"] = json.loads(ann["segmentation_mask"])
+                annotations.append(ann)
+            return annotations
+        finally:
+            conn.close()
+
+    def delete_annotation(self, annotation_id: int) -> bool:
+        """
+        Delete a manual annotation by ID.
+
+        Args:
+            annotation_id: ID of the annotation to delete
+
+        Returns:
+            True if an annotation was deleted, False otherwise.
+        """
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute("DELETE FROM annotations WHERE id = ?", (annotation_id,))
+            conn.commit()
+            return cursor.rowcount > 0
+        finally:
+            conn.close()
+
+    # ==================== Object Class Operations ====================
+
+    def get_object_classes(self) -> List[Dict]:
+        """
+        Get all object classes.
+
+        Returns:
+            List of object class dictionaries
+        """
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute("SELECT * FROM object_classes ORDER BY class_name")
            return [dict(row) for row in cursor.fetchall()]
        finally:
            conn.close()

+    def get_object_class_by_id(self, class_id: int) -> Optional[Dict]:
+        """Get object class by ID."""
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute("SELECT * FROM object_classes WHERE id = ?", (class_id,))
+            row = cursor.fetchone()
+            return dict(row) if row else None
+        finally:
+            conn.close()
+
+    def get_object_class_by_name(self, class_name: str) -> Optional[Dict]:
+        """Get object class by name."""
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute(
+                "SELECT * FROM object_classes WHERE class_name = ?", (class_name,)
+            )
+            row = cursor.fetchone()
+            return dict(row) if row else None
+        finally:
+            conn.close()
+
+    def add_object_class(
+        self, class_name: str, color: str, description: Optional[str] = None
+    ) -> int:
+        """
+        Add a new object class.
+
+        Args:
+            class_name: Name of the object class
+            color: Hex color code (e.g., '#FF0000')
+            description: Optional description
+
+        Returns:
+            ID of the inserted object class
+        """
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute(
+                """
+                INSERT INTO object_classes (class_name, color, description)
+                VALUES (?, ?, ?)
+                """,
+                (class_name, color, description),
+            )
+            conn.commit()
+            return cursor.lastrowid
+        except sqlite3.IntegrityError:
+            # Class already exists
+            existing = self.get_object_class_by_name(class_name)
+            return existing["id"] if existing else None
+        finally:
+            conn.close()
+
+    def update_object_class(
+        self,
+        class_id: int,
+        class_name: Optional[str] = None,
+        color: Optional[str] = None,
+        description: Optional[str] = None,
+    ) -> bool:
+        """
+        Update an object class.
+
+        Args:
+            class_id: ID of the class to update
+            class_name: New class name (optional)
+            color: New color (optional)
+            description: New description (optional)
+
+        Returns:
+            True if updated, False otherwise
+        """
+        conn = self.get_connection()
+        try:
+            updates = {}
+            if class_name is not None:
+                updates["class_name"] = class_name
+            if color is not None:
+                updates["color"] = color
+            if description is not None:
+                updates["description"] = description
+
+            if not updates:
+                return False
+
+            set_clauses = [f"{key} = ?" for key in updates.keys()]
+            params = list(updates.values()) + [class_id]
+
+            query = f"UPDATE object_classes SET {', '.join(set_clauses)} WHERE id = ?"
+            cursor = conn.cursor()
+            cursor.execute(query, params)
+            conn.commit()
+            return cursor.rowcount > 0
+        finally:
+            conn.close()
+
+    def delete_object_class(self, class_id: int) -> bool:
+        """
+        Delete an object class.
+
+        Args:
+            class_id: ID of the class to delete
+
+        Returns:
+            True if deleted, False otherwise
+        """
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute("DELETE FROM object_classes WHERE id = ?", (class_id,))
+            conn.commit()
+            return cursor.rowcount > 0
+        finally:
+            conn.close()
+
    @staticmethod
    def calculate_checksum(file_path: str) -> str:
        """Calculate MD5 checksum of a file."""
--- a/src/database/models.py
+++ b/src/database/models.py
@@ -5,7 +5,7 @@ These dataclasses represent the database entities.

 from dataclasses import dataclass
 from datetime import datetime
-from typing import Optional, Dict, Tuple
+from typing import Optional, Dict, Tuple, List


@dataclass
@@ -46,6 +46,9 @@ class Detection:
    class_name: str
    bbox: Tuple[float, float, float, float]  # (x_min, y_min, x_max, y_max)
    confidence: float
+    segmentation_mask: Optional[
+        List[List[float]]
+    ]  # List of polygon coordinates [[x1,y1], [x2,y2], ...]
    detected_at: datetime
    metadata: Optional[Dict]

@@ -58,6 +61,9 @@ class Annotation:
    image_id: int
    class_name: str
    bbox: Tuple[float, float, float, float]  # (x_min, y_min, x_max, y_max)
+    segmentation_mask: Optional[
+        List[List[float]]
+    ]  # List of polygon coordinates [[x1,y1], [x2,y2], ...]
    annotator: str
    created_at: datetime
    verified: bool
--- a/src/database/schema.sql
+++ b/src/database/schema.sql
@@ -37,25 +37,44 @@ CREATE TABLE IF NOT EXISTS detections (
    x_max REAL NOT NULL CHECK(x_max >= 0 AND x_max <= 1),
    y_max REAL NOT NULL CHECK(y_max >= 0 AND y_max <= 1),
    confidence REAL NOT NULL CHECK(confidence >= 0 AND confidence <= 1),
+    segmentation_mask TEXT,  -- JSON string of polygon coordinates [[x1,y1], [x2,y2], ...]
    detected_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    metadata TEXT,  -- JSON string for additional metadata
    FOREIGN KEY (image_id) REFERENCES images (id) ON DELETE CASCADE,
    FOREIGN KEY (model_id) REFERENCES models (id) ON DELETE CASCADE
 );

-- Annotations table: stores manual annotations (future feature)
+-- Object classes table: stores annotation class definitions with colors
+CREATE TABLE IF NOT EXISTS object_classes (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    class_name TEXT NOT NULL UNIQUE,
+    color TEXT NOT NULL,  -- Hex color code (e.g., '#FF0000')
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    description TEXT
+);
+
+-- Insert default object classes
+INSERT OR IGNORE INTO object_classes (class_name, color, description) VALUES
+    ('cell', '#FF0000', 'Cell object'),
+    ('nucleus', '#00FF00', 'Cell nucleus'),
+    ('mitochondria', '#0000FF', 'Mitochondria'),
+    ('vesicle', '#FFFF00', 'Vesicle');
+
+-- Annotations table: stores manual annotations
 CREATE TABLE IF NOT EXISTS annotations (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    image_id INTEGER NOT NULL,
-    class_name TEXT NOT NULL,
+    class_id INTEGER NOT NULL,
    x_min REAL NOT NULL CHECK(x_min >= 0 AND x_min <= 1),
    y_min REAL NOT NULL CHECK(y_min >= 0 AND y_min <= 1),
    x_max REAL NOT NULL CHECK(x_max >= 0 AND x_max <= 1),
    y_max REAL NOT NULL CHECK(y_max >= 0 AND y_max <= 1),
+    segmentation_mask TEXT,  -- JSON string of polygon coordinates [[x1,y1], [x2,y2], ...]
    annotator TEXT,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    verified BOOLEAN DEFAULT 0,
-    FOREIGN KEY (image_id) REFERENCES images (id) ON DELETE CASCADE
+    FOREIGN KEY (image_id) REFERENCES images (id) ON DELETE CASCADE,
+    FOREIGN KEY (class_id) REFERENCES object_classes (id) ON DELETE CASCADE
 );

 -- Create indexes for performance optimization
@@ -67,4 +86,6 @@ CREATE INDEX IF NOT EXISTS idx_detections_confidence ON detections(confidence);
 CREATE INDEX IF NOT EXISTS idx_images_relative_path ON images(relative_path);
 CREATE INDEX IF NOT EXISTS idx_images_added_at ON images(added_at);
 CREATE INDEX IF NOT EXISTS idx_annotations_image_id ON annotations(image_id);
-CREATE INDEX IF NOT EXISTS idx_models_created_at ON models(created_at);
+CREATE INDEX IF NOT EXISTS idx_annotations_class_id ON annotations(class_id);
+CREATE INDEX IF NOT EXISTS idx_models_created_at ON models(created_at);
+CREATE INDEX IF NOT EXISTS idx_object_classes_class_name ON object_classes(class_name);
--- a/src/gui/dialogs/config_dialog.py
+++ b/src/gui/dialogs/config_dialog.py
@@ -121,7 +121,7 @@ class ConfigDialog(QDialog):
        models_layout.addRow("Models Directory:", self.models_dir_edit)

        self.base_model_edit = QLineEdit()
-        self.base_model_edit.setPlaceholderText("yolov8s.pt")
+        self.base_model_edit.setPlaceholderText("yolov8s-seg.pt")
        models_layout.addRow("Default Base Model:", self.base_model_edit)

        models_group.setLayout(models_layout)
@@ -232,7 +232,7 @@ class ConfigDialog(QDialog):
            self.config_manager.get("models.models_directory", "data/models")
        )
        self.base_model_edit.setText(
-            self.config_manager.get("models.default_base_model", "yolov8s.pt")
+            self.config_manager.get("models.default_base_model", "yolov8s-seg.pt")
        )

        # Training settings
--- a/src/gui/main_window.py
+++ b/src/gui/main_window.py
@@ -13,7 +13,7 @@ from PySide6.QtWidgets import (
    QVBoxLayout,
    QLabel,
 )
-from PySide6.QtCore import Qt, QTimer
+from PySide6.QtCore import Qt, QTimer, QSettings
 from PySide6.QtGui import QAction, QKeySequence

 from src.database.db_manager import DatabaseManager
@@ -52,8 +52,8 @@ class MainWindow(QMainWindow):
        self._create_tab_widget()
        self._create_status_bar()

-        # Center window on screen
-        self._center_window()
+        # Restore window geometry or center window on screen
+        self._restore_window_state()

        logger.info("Main window initialized")

@@ -156,6 +156,24 @@ class MainWindow(QMainWindow):
            (screen.width() - size.width()) // 2, (screen.height() - size.height()) // 2
        )

+    def _restore_window_state(self):
+        """Restore window geometry from settings or center window."""
+        settings = QSettings("microscopy_app", "object_detection")
+        geometry = settings.value("main_window/geometry")
+
+        if geometry:
+            self.restoreGeometry(geometry)
+            logger.debug("Restored window geometry from settings")
+        else:
+            self._center_window()
+            logger.debug("Centered window on screen")
+
+    def _save_window_state(self):
+        """Save window geometry to settings."""
+        settings = QSettings("microscopy_app", "object_detection")
+        settings.setValue("main_window/geometry", self.saveGeometry())
+        logger.debug("Saved window geometry to settings")
+
    def _show_settings(self):
        """Show settings dialog."""
        logger.info("Opening settings dialog")
@@ -276,6 +294,13 @@ class MainWindow(QMainWindow):
        )

        if reply == QMessageBox.Yes:
+            # Save window state before closing
+            self._save_window_state()
+
+            # Save annotation tab state if it exists
+            if hasattr(self, "annotation_tab"):
+                self.annotation_tab.save_state()
+
            logger.info("Application closing")
            event.accept()
        else:
--- a/src/gui/tabs/annotation_tab.py
+++ b/src/gui/tabs/annotation_tab.py
@@ -1,16 +1,33 @@
 """
 Annotation tab for the microscopy object detection application.
-Future feature for manual annotation.
+Manual annotation with pen tool and object class management.
 """

-from PySide6.QtWidgets import QWidget, QVBoxLayout, QLabel, QGroupBox
+from PySide6.QtWidgets import (
+    QWidget,
+    QVBoxLayout,
+    QHBoxLayout,
+    QLabel,
+    QGroupBox,
+    QPushButton,
+    QFileDialog,
+    QMessageBox,
+    QSplitter,
+)
+from PySide6.QtCore import Qt, QSettings
+from pathlib import Path

 from src.database.db_manager import DatabaseManager
 from src.utils.config_manager import ConfigManager
+from src.utils.image import Image, ImageLoadError
+from src.utils.logger import get_logger
+from src.gui.widgets import AnnotationCanvasWidget, AnnotationToolsWidget
+
+logger = get_logger(__name__)


 class AnnotationTab(QWidget):
-    """Annotation tab placeholder (future feature)."""
+    """Annotation tab for manual image annotation."""

    def __init__(
        self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None
@@ -18,6 +35,12 @@ class AnnotationTab(QWidget):
        super().__init__(parent)
        self.db_manager = db_manager
        self.config_manager = config_manager
+        self.current_image = None
+        self.current_image_path = None
+        self.current_image_id = None
+        self.current_annotations = []
+        # IDs of annotations currently selected on the canvas (multi-select)
+        self.selected_annotation_ids = []

        self._setup_ui()

@@ -25,24 +48,519 @@ class AnnotationTab(QWidget):
        """Setup user interface."""
        layout = QVBoxLayout()

-        group = QGroupBox("Annotation Tool (Future Feature)")
-        group_layout = QVBoxLayout()
-        label = QLabel(
-            "Annotation functionality will be implemented in future version.\n\n"
-            "Planned Features:\n"
-            "- Image browser\n"
-            "- Drawing tools for bounding boxes\n"
-            "- Class label assignment\n"
-            "- Export annotations to YOLO format\n"
-            "- Annotation verification"
-        )
-        group_layout.addWidget(label)
-        group.setLayout(group_layout)
+        # Main horizontal splitter to divide left (image) and right (controls)
+        self.main_splitter = QSplitter(Qt.Horizontal)
+        self.main_splitter.setHandleWidth(10)

-        layout.addWidget(group)
-        layout.addStretch()
+        # { Left splitter for image display and zoom info
+        self.left_splitter = QSplitter(Qt.Vertical)
+        self.left_splitter.setHandleWidth(10)
+
+        # Annotation canvas section
+        canvas_group = QGroupBox("Annotation Canvas")
+        canvas_layout = QVBoxLayout()
+
+        # Use the AnnotationCanvasWidget
+        self.annotation_canvas = AnnotationCanvasWidget()
+        self.annotation_canvas.zoom_changed.connect(self._on_zoom_changed)
+        self.annotation_canvas.annotation_drawn.connect(self._on_annotation_drawn)
+        # Selection of existing polylines (when tool is not in drawing mode)
+        self.annotation_canvas.annotation_selected.connect(self._on_annotation_selected)
+        canvas_layout.addWidget(self.annotation_canvas)
+
+        canvas_group.setLayout(canvas_layout)
+        self.left_splitter.addWidget(canvas_group)
+
+        # Controls info
+        controls_info = QLabel(
+            "Zoom: Mouse wheel or +/- keys | Drawing: Enable pen and drag mouse"
+        )
+        controls_info.setStyleSheet("QLabel { color: #888; font-style: italic; }")
+        self.left_splitter.addWidget(controls_info)
+        # }
+
+        # { Right splitter for annotation tools and controls
+        self.right_splitter = QSplitter(Qt.Vertical)
+        self.right_splitter.setHandleWidth(10)
+
+        # Annotation tools section
+        self.annotation_tools = AnnotationToolsWidget(self.db_manager)
+        self.annotation_tools.polyline_enabled_changed.connect(
+            self.annotation_canvas.set_polyline_enabled
+        )
+        self.annotation_tools.polyline_pen_color_changed.connect(
+            self.annotation_canvas.set_polyline_pen_color
+        )
+        self.annotation_tools.polyline_pen_width_changed.connect(
+            self.annotation_canvas.set_polyline_pen_width
+        )
+        # Show / hide bounding boxes
+        self.annotation_tools.show_bboxes_changed.connect(
+            self.annotation_canvas.set_show_bboxes
+        )
+        # RDP simplification controls
+        self.annotation_tools.simplify_on_finish_changed.connect(
+            self._on_simplify_on_finish_changed
+        )
+        self.annotation_tools.simplify_epsilon_changed.connect(
+            self._on_simplify_epsilon_changed
+        )
+        # Class selection and class-color changes
+        self.annotation_tools.class_selected.connect(self._on_class_selected)
+        self.annotation_tools.class_color_changed.connect(self._on_class_color_changed)
+        self.annotation_tools.clear_annotations_requested.connect(
+            self._on_clear_annotations
+        )
+        # Delete selected annotation on canvas
+        self.annotation_tools.delete_selected_annotation_requested.connect(
+            self._on_delete_selected_annotation
+        )
+        self.right_splitter.addWidget(self.annotation_tools)
+
+        # Image loading section
+        load_group = QGroupBox("Image Loading")
+        load_layout = QVBoxLayout()
+
+        # Load image button
+        button_layout = QHBoxLayout()
+        self.load_image_btn = QPushButton("Load Image")
+        self.load_image_btn.clicked.connect(self._load_image)
+        button_layout.addWidget(self.load_image_btn)
+        button_layout.addStretch()
+        load_layout.addLayout(button_layout)
+
+        # Image info label
+        self.image_info_label = QLabel("No image loaded")
+        load_layout.addWidget(self.image_info_label)
+
+        load_group.setLayout(load_layout)
+        self.right_splitter.addWidget(load_group)
+        # }
+
+        # Add both splitters to the main horizontal splitter
+        self.main_splitter.addWidget(self.left_splitter)
+        self.main_splitter.addWidget(self.right_splitter)
+
+        # Set initial sizes: 75% for left (image), 25% for right (controls)
+        self.main_splitter.setSizes([750, 250])
+
+        layout.addWidget(self.main_splitter)
        self.setLayout(layout)

+        # Restore splitter positions from settings
+        self._restore_state()
+
+    def _load_image(self):
+        """Load and display an image file."""
+        # Get last opened directory from QSettings
+        settings = QSettings("microscopy_app", "object_detection")
+        last_dir = settings.value("annotation_tab/last_directory", None)
+
+        # Fallback to image repository path or home directory
+        if last_dir and Path(last_dir).exists():
+            start_dir = last_dir
+        else:
+            repo_path = self.config_manager.get_image_repository_path()
+            start_dir = repo_path if repo_path else str(Path.home())
+
+        # Open file dialog
+        file_path, _ = QFileDialog.getOpenFileName(
+            self,
+            "Select Image",
+            start_dir,
+            "Images (*.jpg *.jpeg *.png *.tif *.tiff *.bmp)",
+        )
+
+        if not file_path:
+            return
+
+        try:
+            # Load image using Image class
+            self.current_image = Image(file_path)
+            self.current_image_path = file_path
+
+            # Store the directory for next time
+            settings.setValue(
+                "annotation_tab/last_directory", str(Path(file_path).parent)
+            )
+
+            # Get or create image in database
+            relative_path = str(Path(file_path).name)  # Simplified for now
+            self.current_image_id = self.db_manager.get_or_create_image(
+                relative_path,
+                Path(file_path).name,
+                self.current_image.width,
+                self.current_image.height,
+            )
+
+            # Display image using the AnnotationCanvasWidget
+            self.annotation_canvas.load_image(self.current_image)
+
+            # Load and display any existing annotations for this image
+            self._load_annotations_for_current_image()
+
+            # Update info label
+            self._update_image_info()
+
+            logger.info(f"Loaded image: {file_path} (DB ID: {self.current_image_id})")
+
+        except ImageLoadError as e:
+            logger.error(f"Failed to load image: {e}")
+            QMessageBox.critical(
+                self, "Error Loading Image", f"Failed to load image:\n{str(e)}"
+            )
+        except Exception as e:
+            logger.error(f"Unexpected error loading image: {e}")
+            QMessageBox.critical(self, "Error", f"Unexpected error:\n{str(e)}")
+
+    def _update_image_info(self):
+        """Update the image info label with current image details."""
+        if self.current_image is None:
+            self.image_info_label.setText("No image loaded")
+            return
+
+        zoom_percentage = self.annotation_canvas.get_zoom_percentage()
+        info_text = (
+            f"File: {Path(self.current_image_path).name}\n"
+            f"Size: {self.current_image.width}x{self.current_image.height} pixels\n"
+            f"Channels: {self.current_image.channels}\n"
+            f"Data type: {self.current_image.dtype}\n"
+            f"Format: {self.current_image.format.upper()}\n"
+            f"File size: {self.current_image.size_mb:.2f} MB\n"
+            f"Zoom: {zoom_percentage}%"
+        )
+        self.image_info_label.setText(info_text)
+
+    def _on_zoom_changed(self, zoom_scale: float):
+        """Handle zoom level changes from the annotation canvas."""
+        self._update_image_info()
+
+    def _on_annotation_drawn(self, points: list):
+        """
+        Handle when an annotation stroke is drawn.
+
+        Saves the new annotation directly to the database and refreshes the
+        on-canvas display of annotations for the current image.
+        """
+        # Ensure we have an image loaded and in the DB
+        if not self.current_image or not self.current_image_id:
+            logger.warning("Annotation drawn but no image loaded")
+            QMessageBox.warning(
+                self,
+                "No Image",
+                "Please load an image before drawing annotations.",
+            )
+            return
+
+        current_class = self.annotation_tools.get_current_class()
+
+        if not current_class:
+            logger.warning("Annotation drawn but no object class selected")
+            QMessageBox.warning(
+                self,
+                "No Class Selected",
+                "Please select an object class before drawing annotations.",
+            )
+            return
+
+        if not points:
+            logger.warning("Annotation drawn with no points, ignoring")
+            return
+
+        # points are [(x_norm, y_norm), ...]
+        xs = [p[0] for p in points]
+        ys = [p[1] for p in points]
+        x_min, x_max = min(xs), max(xs)
+        y_min, y_max = min(ys), max(ys)
+
+        # Store segmentation mask in [y_norm, x_norm] format to match DB
+        db_polyline = [[float(y), float(x)] for (x, y) in points]
+
+        try:
+            annotation_id = self.db_manager.add_annotation(
+                image_id=self.current_image_id,
+                class_id=current_class["id"],
+                bbox=(x_min, y_min, x_max, y_max),
+                annotator="manual",
+                segmentation_mask=db_polyline,
+                verified=False,
+            )
+
+            logger.info(
+                f"Saved annotation (ID: {annotation_id}) for class "
+                f"'{current_class['class_name']}' "
+                f"Bounding box: ({x_min:.3f}, {y_min:.3f}) to ({x_max:.3f}, {y_max:.3f})\n"
+                f"with {len(points)} polyline points"
+            )
+
+            # Reload annotations from DB and redraw (respecting current class filter)
+            self._load_annotations_for_current_image()
+
+        except Exception as e:
+            logger.error(f"Failed to save annotation: {e}")
+            QMessageBox.critical(self, "Error", f"Failed to save annotation:\n{str(e)}")
+
+    def _on_annotation_selected(self, annotation_ids):
+        """
+        Handle selection of existing annotations on the canvas.
+
+        Args:
+            annotation_ids: List of selected annotation IDs, or None/empty if cleared.
+        """
+        if not annotation_ids:
+            self.selected_annotation_ids = []
+            self.annotation_tools.set_has_selected_annotation(False)
+            logger.debug("Annotation selection cleared on canvas")
+            return
+
+        # Normalize to a unique, sorted list of integer IDs
+        ids = sorted({int(aid) for aid in annotation_ids if isinstance(aid, int)})
+        self.selected_annotation_ids = ids
+        self.annotation_tools.set_has_selected_annotation(bool(ids))
+        logger.debug(f"Annotations selected on canvas: IDs={ids}")
+
+    def _on_simplify_on_finish_changed(self, enabled: bool):
+        """Update canvas simplify-on-finish flag from tools widget."""
+        self.annotation_canvas.simplify_on_finish = enabled
+        logger.debug(f"Annotation simplification on finish set to {enabled}")
+
+    def _on_simplify_epsilon_changed(self, epsilon: float):
+        """Update canvas RDP epsilon from tools widget."""
+        self.annotation_canvas.simplify_epsilon = float(epsilon)
+        logger.debug(f"Annotation simplification epsilon set to {epsilon}")
+
+    def _on_class_color_changed(self):
+        """
+        Handle changes to the selected object's class color.
+
+        When the user updates a class color in the tools widget, reload the
+        annotations for the current image so that all polylines are redrawn
+        using the updated per-class colors.
+        """
+        if not self.current_image_id:
+            return
+
+        logger.debug(
+            f"Class color changed; reloading annotations for image ID {self.current_image_id}"
+        )
+        self._load_annotations_for_current_image()
+
+    def _on_class_selected(self, class_data):
+        """
+        Handle when an object class is selected or cleared.
+
+        When a specific class is selected, only annotations of that class are drawn.
+        When the selection is cleared ("-- Select Class --"), all annotations are shown.
+        """
+        if class_data:
+            logger.debug(f"Object class selected: {class_data['class_name']}")
+        else:
+            logger.debug(
+                'No class selected ("-- Select Class --"), showing all annotations'
+            )
+
+        # Changing the class filter invalidates any previous selection
+        self.selected_annotation_ids = []
+        self.annotation_tools.set_has_selected_annotation(False)
+
+        # Whenever the selection changes, update which annotations are visible
+        self._redraw_annotations_for_current_filter()
+
+    def _on_clear_annotations(self):
+        """Handle clearing all annotations."""
+        self.annotation_canvas.clear_annotations()
+        # Clear in-memory state and selection, but keep DB entries unchanged
+        self.current_annotations = []
+        self.selected_annotation_ids = []
+        self.annotation_tools.set_has_selected_annotation(False)
+        logger.info("Cleared all annotations")
+
+    def _on_delete_selected_annotation(self):
+        """Handle deleting the currently selected annotation(s) (if any)."""
+        if not self.selected_annotation_ids:
+            QMessageBox.information(
+                self,
+                "No Selection",
+                "No annotation is currently selected.",
+            )
+            return
+
+        count = len(self.selected_annotation_ids)
+        if count == 1:
+            question = "Are you sure you want to delete the selected annotation?"
+            title = "Delete Annotation"
+        else:
+            question = (
+                f"Are you sure you want to delete the {count} selected annotations?"
+            )
+            title = "Delete Annotations"
+
+        reply = QMessageBox.question(
+            self,
+            title,
+            question,
+            QMessageBox.Yes | QMessageBox.No,
+            QMessageBox.No,
+        )
+        if reply != QMessageBox.Yes:
+            return
+
+        failed_ids = []
+        try:
+            for ann_id in self.selected_annotation_ids:
+                try:
+                    deleted = self.db_manager.delete_annotation(ann_id)
+                    if not deleted:
+                        failed_ids.append(ann_id)
+                except Exception as e:
+                    logger.error(f"Failed to delete annotation ID {ann_id}: {e}")
+                    failed_ids.append(ann_id)
+
+            if failed_ids:
+                QMessageBox.warning(
+                    self,
+                    "Partial Failure",
+                    "Some annotations could not be deleted:\n"
+                    + ", ".join(str(a) for a in failed_ids),
+                )
+            else:
+                logger.info(
+                    f"Deleted {count} annotation(s): "
+                    + ", ".join(str(a) for a in self.selected_annotation_ids)
+                )
+
+            # Clear selection and reload annotations for the current image from DB
+            self.selected_annotation_ids = []
+            self.annotation_tools.set_has_selected_annotation(False)
+            self._load_annotations_for_current_image()
+
+        except Exception as e:
+            logger.error(f"Failed to delete annotations: {e}")
+            QMessageBox.critical(
+                self,
+                "Error",
+                f"Failed to delete annotations:\n{str(e)}",
+            )
+
+    def _load_annotations_for_current_image(self):
+        """
+        Load all annotations for the current image from the database and
+        redraw them on the canvas, honoring the currently selected class
+        filter (if any).
+        """
+        if not self.current_image_id:
+            self.current_annotations = []
+            self.annotation_canvas.clear_annotations()
+            self.selected_annotation_ids = []
+            self.annotation_tools.set_has_selected_annotation(False)
+            return
+
+        try:
+            self.current_annotations = self.db_manager.get_annotations_for_image(
+                self.current_image_id
+            )
+            # New annotations loaded; reset any selection
+            self.selected_annotation_ids = []
+            self.annotation_tools.set_has_selected_annotation(False)
+            self._redraw_annotations_for_current_filter()
+        except Exception as e:
+            logger.error(
+                f"Failed to load annotations for image {self.current_image_id}: {e}"
+            )
+            QMessageBox.critical(
+                self,
+                "Error",
+                f"Failed to load annotations for this image:\n{str(e)}",
+            )
+
+    def _redraw_annotations_for_current_filter(self):
+        """
+        Redraw annotations for the current image, optionally filtered by the
+        currently selected object class.
+        """
+        # Clear current on-canvas annotations but keep the image
+        self.annotation_canvas.clear_annotations()
+
+        if not self.current_annotations:
+            return
+
+        current_class = self.annotation_tools.get_current_class()
+        selected_class_id = current_class["id"] if current_class else None
+
+        drawn_count = 0
+        for ann in self.current_annotations:
+            # Filter by class if one is selected
+            if (
+                selected_class_id is not None
+                and ann.get("class_id") != selected_class_id
+            ):
+                continue
+
+            if ann.get("segmentation_mask"):
+                polyline = ann["segmentation_mask"]
+                color = ann.get("class_color", "#FF0000")
+
+                self.annotation_canvas.draw_saved_polyline(
+                    polyline,
+                    color,
+                    width=3,
+                    annotation_id=ann["id"],
+                )
+                self.annotation_canvas.draw_saved_bbox(
+                    [ann["x_min"], ann["y_min"], ann["x_max"], ann["y_max"]],
+                    color,
+                    width=3,
+                )
+                drawn_count += 1
+
+        logger.info(
+            f"Displayed {drawn_count} annotation(s) for current image with "
+            f"{'no class filter' if selected_class_id is None else f'class_id={selected_class_id}'}"
+        )
+
+    def _restore_state(self):
+        """Restore splitter positions from settings."""
+        settings = QSettings("microscopy_app", "object_detection")
+
+        # Restore main splitter state
+        main_state = settings.value("annotation_tab/main_splitter_state")
+        if main_state:
+            self.main_splitter.restoreState(main_state)
+            logger.debug("Restored main splitter state")
+
+        # Restore left splitter state
+        left_state = settings.value("annotation_tab/left_splitter_state")
+        if left_state:
+            self.left_splitter.restoreState(left_state)
+            logger.debug("Restored left splitter state")
+
+        # Restore right splitter state
+        right_state = settings.value("annotation_tab/right_splitter_state")
+        if right_state:
+            self.right_splitter.restoreState(right_state)
+            logger.debug("Restored right splitter state")
+
+    def save_state(self):
+        """Save splitter positions to settings."""
+        settings = QSettings("microscopy_app", "object_detection")
+
+        # Save main splitter state
+        settings.setValue(
+            "annotation_tab/main_splitter_state", self.main_splitter.saveState()
+        )
+
+        # Save left splitter state
+        settings.setValue(
+            "annotation_tab/left_splitter_state", self.left_splitter.saveState()
+        )
+
+        # Save right splitter state
+        settings.setValue(
+            "annotation_tab/right_splitter_state", self.right_splitter.saveState()
+        )
+
+        logger.debug("Saved annotation tab splitter states")
+
    def refresh(self):
        """Refresh the tab."""
        pass
--- a/src/gui/tabs/detection_tab.py
+++ b/src/gui/tabs/detection_tab.py
@@ -159,7 +159,7 @@ class DetectionTab(QWidget):

            # Add base model option
            base_model = self.config_manager.get(
-                "models.default_base_model", "yolov8s.pt"
+                "models.default_base_model", "yolov8s-seg.pt"
            )
            self.model_combo.addItem(
                f"Base Model ({base_model})", {"id": 0, "path": base_model}
@@ -256,7 +256,7 @@ class DetectionTab(QWidget):
            if model_id == 0:
                # Create database entry for base model
                base_model = self.config_manager.get(
-                    "models.default_base_model", "yolov8s.pt"
+                    "models.default_base_model", "yolov8s-seg.pt"
                )
                model_id = self.db_manager.add_model(
                    model_name="Base Model",
--- a/src/gui/widgets/init.py
+++ b/src/gui/widgets/init.py
@@ -0,0 +1,7 @@
+"""GUI widgets for the microscopy object detection application."""
+
+from src.gui.widgets.image_display_widget import ImageDisplayWidget
+from src.gui.widgets.annotation_canvas_widget import AnnotationCanvasWidget
+from src.gui.widgets.annotation_tools_widget import AnnotationToolsWidget
+
+__all__ = ["ImageDisplayWidget", "AnnotationCanvasWidget", "AnnotationToolsWidget"]
--- a/src/gui/widgets/annotation_canvas_widget.py
+++ b/src/gui/widgets/annotation_canvas_widget.py
@@ -0,0 +1,887 @@
+"""
+Annotation canvas widget for drawing annotations on images.
+Currently supports polyline drawing tool with color selection for manual annotation.
+"""
+
+import numpy as np
+import math
+
+from PySide6.QtWidgets import QWidget, QVBoxLayout, QLabel, QScrollArea
+from PySide6.QtGui import (
+    QPixmap,
+    QImage,
+    QPainter,
+    QPen,
+    QColor,
+    QKeyEvent,
+    QMouseEvent,
+    QPaintEvent,
+)
+from PySide6.QtCore import Qt, QEvent, Signal, QPoint
+from typing import Any, Dict, List, Optional, Tuple
+
+from src.utils.image import Image, ImageLoadError
+from src.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+def perpendicular_distance(
+    point: Tuple[float, float],
+    start: Tuple[float, float],
+    end: Tuple[float, float],
+) -> float:
+    """Perpendicular distance from `point` to the line defined by `start`->`end`."""
+    (x, y), (x1, y1), (x2, y2) = point, start, end
+    dx = x2 - x1
+    dy = y2 - y1
+    if dx == 0.0 and dy == 0.0:
+        return math.hypot(x - x1, y - y1)
+    num = abs(dy * x - dx * y + x2 * y1 - y2 * x1)
+    den = math.hypot(dx, dy)
+    return num / den
+
+
+def rdp(points: List[Tuple[float, float]], epsilon: float) -> List[Tuple[float, float]]:
+    """
+    Recursive Ramer-Douglas-Peucker (RDP) polyline simplification.
+
+    Args:
+        points: List of (x, y) points.
+        epsilon: Maximum allowed perpendicular distance in pixels.
+
+    Returns:
+        Simplified list of (x, y) points including first and last.
+    """
+    if len(points) <= 2:
+        return list(points)
+
+    start = points[0]
+    end = points[-1]
+    max_dist = -1.0
+    index = -1
+
+    for i in range(1, len(points) - 1):
+        d = perpendicular_distance(points[i], start, end)
+        if d > max_dist:
+            max_dist = d
+            index = i
+
+    if max_dist > epsilon:
+        # Recursive split
+        left = rdp(points[: index + 1], epsilon)
+        right = rdp(points[index:], epsilon)
+        # Concatenate but avoid duplicate at split point
+        return left[:-1] + right
+
+    # Keep only start and end
+    return [start, end]
+
+
+def simplify_polyline(
+    points: List[Tuple[float, float]], epsilon: float
+) -> List[Tuple[float, float]]:
+    """
+    Simplify a polyline with RDP while preserving closure semantics.
+
+    If the polyline is closed (first == last), the duplicate last point is removed
+    before simplification and then re-added after simplification.
+    """
+    if not points:
+        return []
+
+    pts = [(float(x), float(y)) for x, y in points]
+    closed = False
+
+    if len(pts) >= 2 and pts[0] == pts[-1]:
+        closed = True
+        pts = pts[:-1]  # remove duplicate last for simplification
+
+    if len(pts) <= 2:
+        simplified = list(pts)
+    else:
+        simplified = rdp(pts, epsilon)
+
+    if closed and simplified:
+        if simplified[0] != simplified[-1]:
+            simplified.append(simplified[0])
+
+    return simplified
+
+
+class AnnotationCanvasWidget(QWidget):
+    """
+    Widget for displaying images and drawing annotations with zoom and drawing tools.
+
+    Features:
+    - Display images with zoom functionality
+    - Polyline tool for drawing annotations
+    - Configurable pen color and width
+    - Mouse-based drawing interface
+    - Zoom in/out with mouse wheel and keyboard
+
+    Signals:
+        zoom_changed: Emitted when zoom level changes (float zoom_scale)
+        annotation_drawn: Emitted when a new stroke is completed (list of points)
+    """
+
+    zoom_changed = Signal(float)
+    annotation_drawn = Signal(list)  # List of (x, y) points in normalized coordinates
+    # Emitted when the user selects an existing polyline on the canvas.
+    # Carries the associated annotation_id (int) or None if selection is cleared
+    annotation_selected = Signal(object)
+
+    def __init__(self, parent=None):
+        """Initialize the annotation canvas widget."""
+        super().__init__(parent)
+
+        self.current_image = None
+        self.original_pixmap = None
+        self.annotation_pixmap = None  # Overlay for annotations
+        self.zoom_scale = 1.0
+        self.zoom_min = 0.1
+        self.zoom_max = 10.0
+        self.zoom_step = 0.1
+        self.zoom_wheel_step = 0.15
+
+        # Drawing / interaction state
+        self.is_drawing = False
+        self.polyline_enabled = False
+        self.polyline_pen_color = QColor(255, 0, 0, 128)  # Default red with 50% alpha
+        self.polyline_pen_width = 3
+        self.show_bboxes: bool = True  # Control visibility of bounding boxes
+
+        # Current stroke and stored polylines (in image coordinates, pixel units)
+        self.current_stroke: List[Tuple[float, float]] = []
+        self.polylines: List[List[Tuple[float, float]]] = []
+        self.stroke_meta: List[Dict[str, Any]] = []  # per-polyline style (color, width)
+        # Optional DB annotation_id for each stored polyline (None for temporary / unsaved)
+        self.polyline_annotation_ids: List[Optional[int]] = []
+        # Indices in self.polylines of the currently selected polylines (multi-select)
+        self.selected_polyline_indices: List[int] = []
+
+        # Stored bounding boxes in normalized coordinates (x_min, y_min, x_max, y_max)
+        self.bboxes: List[List[float]] = []
+        self.bbox_meta: List[Dict[str, Any]] = []  # per-bbox style (color, width)
+
+        # Legacy collection of strokes in normalized coordinates (kept for API compatibility)
+        self.all_strokes: List[dict] = []
+
+        # RDP simplification parameters (in pixels)
+        self.simplify_on_finish: bool = True
+        self.simplify_epsilon: float = 2.0
+        self.sample_threshold: float = 2.0  # minimum movement to sample a new point
+
+        self._setup_ui()
+
+    def _setup_ui(self):
+        """Setup user interface."""
+        layout = QVBoxLayout()
+        layout.setContentsMargins(0, 0, 0, 0)
+
+        # Scroll area for canvas
+        self.scroll_area = QScrollArea()
+        self.scroll_area.setWidgetResizable(True)
+        self.scroll_area.setMinimumHeight(400)
+
+        self.canvas_label = QLabel("No image loaded")
+        self.canvas_label.setAlignment(Qt.AlignCenter)
+        self.canvas_label.setStyleSheet(
+            "QLabel { background-color: #2b2b2b; color: #888; }"
+        )
+        self.canvas_label.setScaledContents(False)
+        self.canvas_label.setMouseTracking(True)
+
+        self.scroll_area.setWidget(self.canvas_label)
+        self.scroll_area.viewport().installEventFilter(self)
+
+        layout.addWidget(self.scroll_area)
+        self.setLayout(layout)
+
+        self.setFocusPolicy(Qt.StrongFocus)
+
+    def load_image(self, image: Image):
+        """
+        Load and display an image.
+
+        Args:
+            image: Image object to display
+        """
+        self.current_image = image
+        self.zoom_scale = 1.0
+        self.clear_annotations()
+        self._display_image()
+        logger.debug(
+            f"Loaded image into annotation canvas: {image.width}x{image.height}"
+        )
+
+    def clear(self):
+        """Clear the displayed image and all annotations."""
+        self.current_image = None
+        self.original_pixmap = None
+        self.annotation_pixmap = None
+        self.zoom_scale = 1.0
+        self.clear_annotations()
+        self.canvas_label.setText("No image loaded")
+        self.canvas_label.setPixmap(QPixmap())
+
+    def clear_annotations(self):
+        """Clear all drawn annotations."""
+        self.all_strokes = []
+        self.current_stroke = []
+        self.polylines = []
+        self.stroke_meta = []
+        self.polyline_annotation_ids = []
+        self.selected_polyline_indices = []
+        self.bboxes = []
+        self.bbox_meta = []
+        self.is_drawing = False
+        if self.annotation_pixmap:
+            self.annotation_pixmap.fill(Qt.transparent)
+            self._update_display()
+
+    def _display_image(self):
+        """Display the current image in the canvas."""
+        if self.current_image is None:
+            return
+
+        try:
+            # Get RGB image data
+            if self.current_image.channels == 3:
+                image_data = self.current_image.get_rgb()
+                height, width, channels = image_data.shape
+            else:
+                image_data = self.current_image.get_grayscale()
+                height, width = image_data.shape
+
+            image_data = np.ascontiguousarray(image_data)
+            bytes_per_line = image_data.strides[0]
+
+            qimage = QImage(
+                image_data.data,
+                width,
+                height,
+                bytes_per_line,
+                self.current_image.qtimage_format,
+            )
+
+            self.original_pixmap = QPixmap.fromImage(qimage)
+
+            # Create transparent overlay for annotations
+            self.annotation_pixmap = QPixmap(self.original_pixmap.size())
+            self.annotation_pixmap.fill(Qt.transparent)
+
+            self._apply_zoom()
+
+        except Exception as e:
+            logger.error(f"Error displaying image: {e}")
+            raise ImageLoadError(f"Failed to display image: {str(e)}")
+
+    def _apply_zoom(self):
+        """Apply current zoom level to the displayed image."""
+        if self.original_pixmap is None:
+            return
+
+        scaled_width = int(self.original_pixmap.width() * self.zoom_scale)
+        scaled_height = int(self.original_pixmap.height() * self.zoom_scale)
+
+        # Scale both image and annotations
+        scaled_image = self.original_pixmap.scaled(
+            scaled_width,
+            scaled_height,
+            Qt.KeepAspectRatio,
+            (
+                Qt.SmoothTransformation
+                if self.zoom_scale >= 1.0
+                else Qt.FastTransformation
+            ),
+        )
+
+        scaled_annotations = self.annotation_pixmap.scaled(
+            scaled_width,
+            scaled_height,
+            Qt.KeepAspectRatio,
+            (
+                Qt.SmoothTransformation
+                if self.zoom_scale >= 1.0
+                else Qt.FastTransformation
+            ),
+        )
+
+        # Composite image and annotations
+        combined = QPixmap(scaled_image.size())
+        painter = QPainter(combined)
+        painter.drawPixmap(0, 0, scaled_image)
+        painter.drawPixmap(0, 0, scaled_annotations)
+        painter.end()
+
+        self.canvas_label.setPixmap(combined)
+        self.canvas_label.setScaledContents(False)
+        self.canvas_label.adjustSize()
+
+        self.zoom_changed.emit(self.zoom_scale)
+
+    def _update_display(self):
+        """Update display after drawing."""
+        self._apply_zoom()
+
+    def set_polyline_enabled(self, enabled: bool):
+        """Enable or disable polyline tool."""
+        self.polyline_enabled = enabled
+        if enabled:
+            self.canvas_label.setCursor(Qt.CrossCursor)
+        else:
+            self.canvas_label.setCursor(Qt.ArrowCursor)
+
+    def set_polyline_pen_color(self, color: QColor):
+        """Set polyline pen color."""
+        self.polyline_pen_color = color
+
+    def set_polyline_pen_width(self, width: int):
+        """Set polyline pen width."""
+        self.polyline_pen_width = max(1, width)
+
+    def get_zoom_percentage(self) -> int:
+        """Get current zoom level as percentage."""
+        return int(self.zoom_scale * 100)
+
+    def zoom_in(self):
+        """Zoom in on the image."""
+        if self.original_pixmap is None:
+            return
+        new_scale = self.zoom_scale + self.zoom_step
+        if new_scale <= self.zoom_max:
+            self.zoom_scale = new_scale
+            self._apply_zoom()
+
+    def zoom_out(self):
+        """Zoom out from the image."""
+        if self.original_pixmap is None:
+            return
+        new_scale = self.zoom_scale - self.zoom_step
+        if new_scale >= self.zoom_min:
+            self.zoom_scale = new_scale
+            self._apply_zoom()
+
+    def reset_zoom(self):
+        """Reset zoom to 100%."""
+        if self.original_pixmap is None:
+            return
+        self.zoom_scale = 1.0
+        self._apply_zoom()
+
+    def _canvas_to_image_coords(self, pos: QPoint) -> Optional[Tuple[int, int]]:
+        """Convert canvas coordinates to image coordinates, accounting for zoom and centering."""
+        if self.original_pixmap is None or self.canvas_label.pixmap() is None:
+            return None
+
+        # Get the displayed pixmap size (after zoom)
+        displayed_pixmap = self.canvas_label.pixmap()
+        displayed_width = displayed_pixmap.width()
+        displayed_height = displayed_pixmap.height()
+
+        # Calculate offset due to label centering (label might be larger than pixmap)
+        label_width = self.canvas_label.width()
+        label_height = self.canvas_label.height()
+        offset_x = max(0, (label_width - displayed_width) // 2)
+        offset_y = max(0, (label_height - displayed_height) // 2)
+
+        # Adjust position for offset and convert to image coordinates
+        x = (pos.x() - offset_x) / self.zoom_scale
+        y = (pos.y() - offset_y) / self.zoom_scale
+
+        # Check bounds
+        if (
+            0 <= x < self.original_pixmap.width()
+            and 0 <= y < self.original_pixmap.height()
+        ):
+            return (int(x), int(y))
+        return None
+
+    def _find_polyline_at(
+        self, img_x: float, img_y: float, threshold_px: float = 5.0
+    ) -> Optional[int]:
+        """
+        Find index of polyline whose geometry is within threshold_px of (img_x, img_y).
+        Returns the index in self.polylines, or None if none is close enough.
+        """
+        best_index: Optional[int] = None
+        best_dist: float = float("inf")
+
+        for idx, polyline in enumerate(self.polylines):
+            if len(polyline) < 2:
+                continue
+
+            # Quick bounding-box check to skip obviously distant polylines
+            xs = [p[0] for p in polyline]
+            ys = [p[1] for p in polyline]
+            if img_x < min(xs) - threshold_px or img_x > max(xs) + threshold_px:
+                continue
+            if img_y < min(ys) - threshold_px or img_y > max(ys) + threshold_px:
+                continue
+
+            # Precise distance to all segments
+            for (x1, y1), (x2, y2) in zip(polyline[:-1], polyline[1:]):
+                d = perpendicular_distance(
+                    (img_x, img_y), (float(x1), float(y1)), (float(x2), float(y2))
+                )
+                if d < best_dist:
+                    best_dist = d
+                    best_index = idx
+
+        if best_index is not None and best_dist <= threshold_px:
+            return best_index
+        return None
+
+    def _image_to_normalized_coords(self, x: int, y: int) -> Tuple[float, float]:
+        """Convert image coordinates to normalized coordinates (0-1)."""
+        if self.original_pixmap is None:
+            return (0.0, 0.0)
+
+        norm_x = x / self.original_pixmap.width()
+        norm_y = y / self.original_pixmap.height()
+        return (norm_x, norm_y)
+
+    def _add_polyline(
+        self,
+        img_points: List[Tuple[float, float]],
+        color: QColor,
+        width: int,
+        annotation_id: Optional[int] = None,
+    ):
+        """Store a polyline in image coordinates and redraw annotations."""
+        if not img_points or len(img_points) < 2:
+            return
+
+        # Ensure all points are tuples of floats
+        normalized_points = [(float(x), float(y)) for x, y in img_points]
+        self.polylines.append(normalized_points)
+        self.stroke_meta.append({"color": QColor(color), "width": int(width)})
+        self.polyline_annotation_ids.append(annotation_id)
+
+        self._redraw_annotations()
+
+    def _redraw_annotations(self):
+        """Redraw all stored polylines and (optionally) bounding boxes onto the annotation pixmap."""
+        if self.annotation_pixmap is None:
+            return
+
+        # Clear existing overlay
+        self.annotation_pixmap.fill(Qt.transparent)
+
+        painter = QPainter(self.annotation_pixmap)
+
+        # Draw polylines
+        for idx, (polyline, meta) in enumerate(zip(self.polylines, self.stroke_meta)):
+            pen_color: QColor = meta.get("color", self.polyline_pen_color)
+            width: int = meta.get("width", self.polyline_pen_width)
+
+            if idx in self.selected_polyline_indices:
+                # Highlight selected polylines in a distinct color / width
+                highlight_color = QColor(255, 255, 0, 200)  # yellow, semi-opaque
+                pen = QPen(
+                    highlight_color,
+                    width + 1,
+                    Qt.SolidLine,
+                    Qt.RoundCap,
+                    Qt.RoundJoin,
+                )
+            else:
+                pen = QPen(
+                    pen_color,
+                    width,
+                    Qt.SolidLine,
+                    Qt.RoundCap,
+                    Qt.RoundJoin,
+                )
+
+            painter.setPen(pen)
+            for (x1, y1), (x2, y2) in zip(polyline[:-1], polyline[1:]):
+                painter.drawLine(int(x1), int(y1), int(x2), int(y2))
+
+        # Draw bounding boxes (dashed) if enabled
+        if self.show_bboxes and self.original_pixmap is not None and self.bboxes:
+            img_width = float(self.original_pixmap.width())
+            img_height = float(self.original_pixmap.height())
+
+            for bbox, meta in zip(self.bboxes, self.bbox_meta):
+                if len(bbox) != 4:
+                    continue
+
+                x_min_norm, y_min_norm, x_max_norm, y_max_norm = bbox
+                x_min = int(x_min_norm * img_width)
+                y_min = int(y_min_norm * img_height)
+                x_max = int(x_max_norm * img_width)
+                y_max = int(y_max_norm * img_height)
+
+                rect_width = x_max - x_min
+                rect_height = y_max - y_min
+
+                pen_color: QColor = meta.get("color", QColor(255, 0, 0, 128))
+                width: int = meta.get("width", self.polyline_pen_width)
+                pen = QPen(
+                    pen_color,
+                    width,
+                    Qt.DashLine,
+                    Qt.SquareCap,
+                    Qt.MiterJoin,
+                )
+                painter.setPen(pen)
+                painter.drawRect(x_min, y_min, rect_width, rect_height)
+
+        painter.end()
+
+        self._update_display()
+
+    def mousePressEvent(self, event: QMouseEvent):
+        """Handle mouse press events for drawing and selecting polylines."""
+        if self.annotation_pixmap is None:
+            super().mousePressEvent(event)
+            return
+
+        # Map click to image coordinates
+        label_pos = self.canvas_label.mapFromGlobal(event.globalPos())
+        img_coords = self._canvas_to_image_coords(label_pos)
+
+        # Left button + drawing tool enabled -> start a new stroke
+        if event.button() == Qt.LeftButton and self.polyline_enabled:
+            if img_coords:
+                self.is_drawing = True
+                self.current_stroke = [(float(img_coords[0]), float(img_coords[1]))]
+            return
+
+        # Left button + drawing tool disabled -> attempt selection of existing polyline
+        if event.button() == Qt.LeftButton and not self.polyline_enabled:
+            if img_coords:
+                idx = self._find_polyline_at(float(img_coords[0]), float(img_coords[1]))
+                if idx is not None:
+                    if event.modifiers() & Qt.ShiftModifier:
+                        # Multi-select mode: add to current selection (if not already selected)
+                        if idx not in self.selected_polyline_indices:
+                            self.selected_polyline_indices.append(idx)
+                    else:
+                        # Single-select mode: replace current selection
+                        self.selected_polyline_indices = [idx]
+
+                    # Build list of selected annotation IDs (ignore None entries)
+                    selected_ids: List[int] = []
+                    for sel_idx in self.selected_polyline_indices:
+                        if 0 <= sel_idx < len(self.polyline_annotation_ids):
+                            ann_id = self.polyline_annotation_ids[sel_idx]
+                            if isinstance(ann_id, int):
+                                selected_ids.append(ann_id)
+
+                    if selected_ids:
+                        self.annotation_selected.emit(selected_ids)
+                    else:
+                        # No valid DB-backed annotations in selection
+                        self.annotation_selected.emit(None)
+                else:
+                    # Clicked on empty space -> clear selection
+                    self.selected_polyline_indices = []
+                    self.annotation_selected.emit(None)
+
+                self._redraw_annotations()
+            return
+
+        # Fallback for other buttons / cases
+        super().mousePressEvent(event)
+
+    def mouseMoveEvent(self, event: QMouseEvent):
+        """Handle mouse move events for drawing."""
+        if (
+            not self.is_drawing
+            or not self.polyline_enabled
+            or self.annotation_pixmap is None
+        ):
+            super().mouseMoveEvent(event)
+            return
+
+        # Get accurate position using global coordinates
+        label_pos = self.canvas_label.mapFromGlobal(event.globalPos())
+        img_coords = self._canvas_to_image_coords(label_pos)
+
+        if img_coords and len(self.current_stroke) > 0:
+            last_point = self.current_stroke[-1]
+            dx = img_coords[0] - last_point[0]
+            dy = img_coords[1] - last_point[1]
+
+            # Only sample a new point if we moved enough pixels
+            if math.hypot(dx, dy) < self.sample_threshold:
+                return
+
+            # Draw line from last point to current point for interactive feedback
+            painter = QPainter(self.annotation_pixmap)
+            pen = QPen(
+                self.polyline_pen_color,
+                self.polyline_pen_width,
+                Qt.SolidLine,
+                Qt.RoundCap,
+                Qt.RoundJoin,
+            )
+            painter.setPen(pen)
+            painter.drawLine(
+                int(last_point[0]),
+                int(last_point[1]),
+                int(img_coords[0]),
+                int(img_coords[1]),
+            )
+            painter.end()
+
+            self.current_stroke.append((float(img_coords[0]), float(img_coords[1])))
+            self._update_display()
+
+    def mouseReleaseEvent(self, event: QMouseEvent):
+        """Handle mouse release events to complete a stroke."""
+        if not self.is_drawing or event.button() != Qt.LeftButton:
+            super().mouseReleaseEvent(event)
+            return
+
+        self.is_drawing = False
+
+        if len(self.current_stroke) > 1 and self.original_pixmap is not None:
+            # Ensure the stroke is closed by connecting end -> start
+            raw_points = list(self.current_stroke)
+            if raw_points[0] != raw_points[-1]:
+                raw_points.append(raw_points[0])
+
+            # Optional RDP simplification (in image pixel space)
+            if self.simplify_on_finish:
+                simplified = simplify_polyline(raw_points, self.simplify_epsilon)
+            else:
+                simplified = raw_points
+
+            if len(simplified) >= 2:
+                # Store polyline and redraw all annotations
+                self._add_polyline(
+                    simplified, self.polyline_pen_color, self.polyline_pen_width
+                )
+
+                # Convert to normalized coordinates for metadata + signal
+                normalized_stroke = [
+                    self._image_to_normalized_coords(int(x), int(y))
+                    for (x, y) in simplified
+                ]
+                self.all_strokes.append(
+                    {
+                        "points": normalized_stroke,
+                        "color": self.polyline_pen_color.name(),
+                        "alpha": self.polyline_pen_color.alpha(),
+                        "width": self.polyline_pen_width,
+                    }
+                )
+
+                # Emit signal with normalized coordinates
+                self.annotation_drawn.emit(normalized_stroke)
+                logger.debug(
+                    f"Completed stroke with {len(simplified)} points "
+                    f"(normalized len={len(normalized_stroke)})"
+                )
+
+        self.current_stroke = []
+
+    def get_all_strokes(self) -> List[dict]:
+        """Get all drawn strokes with metadata."""
+        return self.all_strokes
+
+    def get_annotation_parameters(self) -> Optional[List[Dict[str, Any]]]:
+        """
+        Get all annotation parameters including bounding box and polyline.
+
+        Returns:
+            List of dictionaries, each containing:
+            - 'bbox': [x_min, y_min, x_max, y_max] in normalized image coordinates
+            - 'polyline': List of [y_norm, x_norm] points describing the polygon
+        """
+        if self.original_pixmap is None or not self.polylines:
+            return None
+
+        img_width = float(self.original_pixmap.width())
+        img_height = float(self.original_pixmap.height())
+
+        params: List[Dict[str, Any]] = []
+
+        for idx, polyline in enumerate(self.polylines):
+            if len(polyline) < 2:
+                continue
+
+            xs = [p[0] for p in polyline]
+            ys = [p[1] for p in polyline]
+
+            x_min_norm = min(xs) / img_width
+            x_max_norm = max(xs) / img_width
+            y_min_norm = min(ys) / img_height
+            y_max_norm = max(ys) / img_height
+
+            # Store polyline as [y_norm, x_norm] to match DB convention and
+            # the expectations of draw_saved_polyline().
+            normalized_polyline = [
+                [y / img_height, x / img_width] for (x, y) in polyline
+            ]
+
+            logger.debug(
+                f"Polyline {idx}: {len(polyline)} points, "
+                f"bbox=({x_min_norm:.3f}, {y_min_norm:.3f})-({x_max_norm:.3f}, {y_max_norm:.3f})"
+            )
+
+            params.append(
+                {
+                    "bbox": [x_min_norm, y_min_norm, x_max_norm, y_max_norm],
+                    "polyline": normalized_polyline,
+                }
+            )
+
+        return params or None
+
+    def draw_saved_polyline(
+        self,
+        polyline: List[List[float]],
+        color: str,
+        width: int = 3,
+        annotation_id: Optional[int] = None,
+    ):
+        """
+        Draw a polyline from database coordinates onto the annotation canvas.
+
+        Args:
+            polyline: List of [x, y] coordinate pairs in normalized coordinates (0-1)
+            color: Color hex string (e.g., '#FF0000')
+            width: Line width in pixels
+        """
+        if not self.annotation_pixmap or not self.original_pixmap:
+            logger.warning("Cannot draw polyline: no image loaded")
+            return
+
+        if len(polyline) < 2:
+            logger.warning("Polyline has less than 2 points, cannot draw")
+            return
+
+        # Convert normalized coordinates to image coordinates
+        # Polyline is stored as [[y_norm, x_norm], ...] (row_norm, col_norm format)
+        img_width = self.original_pixmap.width()
+        img_height = self.original_pixmap.height()
+
+        logger.debug(f"Loading polyline with {len(polyline)} points")
+        logger.debug(f"  Image size: {img_width}x{img_height}")
+        logger.debug(f"  First 3 normalized points from DB: {polyline[:3]}")
+
+        img_coords: List[Tuple[float, float]] = []
+        for y_norm, x_norm in polyline:
+            x = float(x_norm * img_width)
+            y = float(y_norm * img_height)
+            img_coords.append((x, y))
+
+        logger.debug(f"  First 3 pixel coords: {img_coords[:3]}")
+
+        # Store and redraw using common pipeline
+        pen_color = QColor(color)
+        pen_color.setAlpha(128)  # Add semi-transparency
+        self._add_polyline(img_coords, pen_color, width, annotation_id=annotation_id)
+
+        # Store in all_strokes for consistency (uses normalized coordinates)
+        self.all_strokes.append(
+            {"points": polyline, "color": color, "alpha": 128, "width": width}
+        )
+
+        logger.debug(
+            f"Drew saved polyline with {len(polyline)} points in color {color}"
+        )
+
+    def draw_saved_bbox(self, bbox: List[float], color: str, width: int = 3):
+        """
+        Draw a bounding box from database coordinates onto the annotation canvas.
+
+        Args:
+            bbox: Bounding box as [x_min_norm, y_min_norm, x_max_norm, y_max_norm]
+                  in normalized coordinates (0-1)
+            color: Color hex string (e.g., '#FF0000')
+            width: Line width in pixels
+        """
+        if not self.annotation_pixmap or not self.original_pixmap:
+            logger.warning("Cannot draw bounding box: no image loaded")
+            return
+
+        if len(bbox) != 4:
+            logger.warning(
+                f"Invalid bounding box format: expected 4 values, got {len(bbox)}"
+            )
+            return
+
+        # Convert normalized coordinates to image coordinates (for logging/debug)
+        img_width = self.original_pixmap.width()
+        img_height = self.original_pixmap.height()
+
+        x_min_norm, y_min_norm, x_max_norm, y_max_norm = bbox
+        x_min = int(x_min_norm * img_width)
+        y_min = int(y_min_norm * img_height)
+        x_max = int(x_max_norm * img_width)
+        y_max = int(y_max_norm * img_height)
+
+        logger.debug(f"Drawing bounding box: {bbox}")
+        logger.debug(f"  Image size: {img_width}x{img_height}")
+        logger.debug(f"  Pixel coords: ({x_min}, {y_min}) to ({x_max}, {y_max})")
+
+        # Store bounding box (normalized) and its style; actual drawing happens
+        # in _redraw_annotations() together with all polylines.
+        pen_color = QColor(color)
+        pen_color.setAlpha(128)  # Add semi-transparency
+        self.bboxes.append(
+            [float(x_min_norm), float(y_min_norm), float(x_max_norm), float(y_max_norm)]
+        )
+        self.bbox_meta.append({"color": pen_color, "width": int(width)})
+
+        # Store in all_strokes for consistency
+        self.all_strokes.append(
+            {"bbox": bbox, "color": color, "alpha": 128, "width": width}
+        )
+
+        # Redraw overlay (polylines + all bounding boxes)
+        self._redraw_annotations()
+        logger.debug(f"Drew saved bounding box in color {color}")
+
+    def set_show_bboxes(self, show: bool):
+        """
+        Enable or disable drawing of bounding boxes.
+
+        Args:
+            show: If True, draw bounding boxes; if False, hide them.
+        """
+        self.show_bboxes = bool(show)
+        logger.debug(f"Set show_bboxes to {self.show_bboxes}")
+        self._redraw_annotations()
+
+    def keyPressEvent(self, event: QKeyEvent):
+        """Handle keyboard events for zooming."""
+        if event.key() in (Qt.Key_Plus, Qt.Key_Equal):
+            self.zoom_in()
+            event.accept()
+        elif event.key() == Qt.Key_Minus:
+            self.zoom_out()
+            event.accept()
+        elif event.key() == Qt.Key_0 and event.modifiers() == Qt.ControlModifier:
+            self.reset_zoom()
+            event.accept()
+        else:
+            super().keyPressEvent(event)
+
+    def eventFilter(self, obj, event: QEvent) -> bool:
+        """Event filter to capture wheel events for zooming."""
+        if event.type() == QEvent.Wheel:
+            wheel_event = event
+            if self.original_pixmap is not None:
+                delta = wheel_event.angleDelta().y()
+
+                if delta > 0:
+                    new_scale = self.zoom_scale + self.zoom_wheel_step
+                    if new_scale <= self.zoom_max:
+                        self.zoom_scale = new_scale
+                        self._apply_zoom()
+                else:
+                    new_scale = self.zoom_scale - self.zoom_wheel_step
+                    if new_scale >= self.zoom_min:
+                        self.zoom_scale = new_scale
+                        self._apply_zoom()
+
+                return True
+
+        return super().eventFilter(obj, event)
--- a/src/gui/widgets/annotation_tools_widget.py
+++ b/src/gui/widgets/annotation_tools_widget.py
@@ -0,0 +1,478 @@
+"""
+Annotation tools widget for controlling annotation parameters.
+Includes polyline tool, color picker, class selection, and annotation management.
+"""
+
+from PySide6.QtWidgets import (
+    QWidget,
+    QVBoxLayout,
+    QHBoxLayout,
+    QLabel,
+    QGroupBox,
+    QPushButton,
+    QComboBox,
+    QSpinBox,
+    QDoubleSpinBox,
+    QCheckBox,
+    QColorDialog,
+    QInputDialog,
+    QMessageBox,
+)
+from PySide6.QtGui import QColor, QIcon, QPixmap, QPainter
+from PySide6.QtCore import Qt, Signal
+from typing import Optional, Dict
+
+from src.database.db_manager import DatabaseManager
+from src.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class AnnotationToolsWidget(QWidget):
+    """
+    Widget for annotation tool controls.
+
+    Features:
+    - Enable/disable polyline tool
+    - Color selection for polyline pen
+    - Object class selection
+    - Add new object classes
+    - Pen width control
+    - Clear annotations
+
+    Signals:
+        polyline_enabled_changed: Emitted when polyline tool is enabled/disabled (bool)
+        polyline_pen_color_changed: Emitted when polyline pen color changes (QColor)
+        polyline_pen_width_changed: Emitted when polyline pen width changes (int)
+        class_selected: Emitted when object class is selected (dict)
+        clear_annotations_requested: Emitted when clear button is pressed
+    """
+
+    polyline_enabled_changed = Signal(bool)
+    polyline_pen_color_changed = Signal(QColor)
+    polyline_pen_width_changed = Signal(int)
+    simplify_on_finish_changed = Signal(bool)
+    simplify_epsilon_changed = Signal(float)
+    # Toggle visibility of bounding boxes on the canvas
+    show_bboxes_changed = Signal(bool)
+    class_selected = Signal(dict)
+    class_color_changed = Signal()
+    clear_annotations_requested = Signal()
+    # Request deletion of the currently selected annotation on the canvas
+    delete_selected_annotation_requested = Signal()
+
+    def __init__(self, db_manager: DatabaseManager, parent=None):
+        """
+        Initialize annotation tools widget.
+
+        Args:
+            db_manager: Database manager instance
+            parent: Parent widget
+        """
+        super().__init__(parent)
+        self.db_manager = db_manager
+        self.polyline_enabled = False
+        self.current_color = QColor(255, 0, 0, 128)  # Red with 50% alpha
+        self.current_class = None
+
+        self._setup_ui()
+        self._load_object_classes()
+
+    def _setup_ui(self):
+        """Setup user interface."""
+        layout = QVBoxLayout()
+
+        # Polyline Tool Group
+        polyline_group = QGroupBox("Polyline Tool")
+        polyline_layout = QVBoxLayout()
+
+        # Enable/Disable polyline tool
+        button_layout = QHBoxLayout()
+        self.polyline_toggle_btn = QPushButton("Start Drawing Polyline")
+        self.polyline_toggle_btn.setCheckable(True)
+        self.polyline_toggle_btn.clicked.connect(self._on_polyline_toggle)
+        button_layout.addWidget(self.polyline_toggle_btn)
+        polyline_layout.addLayout(button_layout)
+
+        # Polyline pen width control
+        width_layout = QHBoxLayout()
+        width_layout.addWidget(QLabel("Pen Width:"))
+        self.polyline_pen_width_spin = QSpinBox()
+        self.polyline_pen_width_spin.setMinimum(1)
+        self.polyline_pen_width_spin.setMaximum(20)
+        self.polyline_pen_width_spin.setValue(3)
+        self.polyline_pen_width_spin.valueChanged.connect(
+            self._on_polyline_pen_width_changed
+        )
+        width_layout.addWidget(self.polyline_pen_width_spin)
+        width_layout.addStretch()
+        polyline_layout.addLayout(width_layout)
+
+        # Simplification controls (RDP)
+        simplify_layout = QHBoxLayout()
+        self.simplify_checkbox = QCheckBox("Simplify on finish")
+        self.simplify_checkbox.setChecked(True)
+        self.simplify_checkbox.stateChanged.connect(self._on_simplify_toggle)
+        simplify_layout.addWidget(self.simplify_checkbox)
+
+        simplify_layout.addWidget(QLabel("epsilon (px):"))
+        self.eps_spin = QDoubleSpinBox()
+        self.eps_spin.setRange(0.0, 1000.0)
+        self.eps_spin.setSingleStep(0.5)
+        self.eps_spin.setValue(2.0)
+        self.eps_spin.valueChanged.connect(self._on_eps_change)
+        simplify_layout.addWidget(self.eps_spin)
+        simplify_layout.addStretch()
+        polyline_layout.addLayout(simplify_layout)
+
+        polyline_group.setLayout(polyline_layout)
+        layout.addWidget(polyline_group)
+
+        # Object Class Group
+        class_group = QGroupBox("Object Class")
+        class_layout = QVBoxLayout()
+
+        # Class selection dropdown
+        self.class_combo = QComboBox()
+        self.class_combo.currentIndexChanged.connect(self._on_class_selected)
+        class_layout.addWidget(self.class_combo)
+
+        # Add / manage classes
+        class_button_layout = QHBoxLayout()
+        self.add_class_btn = QPushButton("Add New Class")
+        self.add_class_btn.clicked.connect(self._on_add_class)
+        class_button_layout.addWidget(self.add_class_btn)
+
+        self.refresh_classes_btn = QPushButton("Refresh")
+        self.refresh_classes_btn.clicked.connect(self._load_object_classes)
+        class_button_layout.addWidget(self.refresh_classes_btn)
+        class_layout.addLayout(class_button_layout)
+
+        # Class color (associated with selected object class)
+        color_layout = QHBoxLayout()
+        color_layout.addWidget(QLabel("Class Color:"))
+        self.color_btn = QPushButton()
+        self.color_btn.setFixedSize(40, 30)
+        self.color_btn.clicked.connect(self._on_color_picker)
+        self._update_color_button()
+        color_layout.addWidget(self.color_btn)
+        color_layout.addStretch()
+        class_layout.addLayout(color_layout)
+
+        # Selected class info
+        self.class_info_label = QLabel("No class selected")
+        self.class_info_label.setWordWrap(True)
+        self.class_info_label.setStyleSheet(
+            "QLabel { color: #888; font-style: italic; }"
+        )
+        class_layout.addWidget(self.class_info_label)
+
+        class_group.setLayout(class_layout)
+        layout.addWidget(class_group)
+
+        # Actions Group
+        actions_group = QGroupBox("Actions")
+        actions_layout = QVBoxLayout()
+
+        # Show / hide bounding boxes
+        self.show_bboxes_checkbox = QCheckBox("Show bounding boxes")
+        self.show_bboxes_checkbox.setChecked(True)
+        self.show_bboxes_checkbox.stateChanged.connect(self._on_show_bboxes_toggle)
+        actions_layout.addWidget(self.show_bboxes_checkbox)
+
+        self.clear_btn = QPushButton("Clear All Annotations")
+        self.clear_btn.clicked.connect(self._on_clear_annotations)
+        actions_layout.addWidget(self.clear_btn)
+
+        # Delete currently selected annotation (enabled when a selection exists)
+        self.delete_selected_btn = QPushButton("Delete Selected Annotation")
+        self.delete_selected_btn.clicked.connect(self._on_delete_selected_annotation)
+        self.delete_selected_btn.setEnabled(False)
+        actions_layout.addWidget(self.delete_selected_btn)
+
+        actions_group.setLayout(actions_layout)
+        layout.addWidget(actions_group)
+
+        layout.addStretch()
+        self.setLayout(layout)
+
+    def _update_color_button(self):
+        """Update the color button appearance with current color."""
+        pixmap = QPixmap(40, 30)
+        pixmap.fill(self.current_color)
+
+        # Add border
+        painter = QPainter(pixmap)
+        painter.setPen(Qt.black)
+        painter.drawRect(0, 0, pixmap.width() - 1, pixmap.height() - 1)
+        painter.end()
+
+        self.color_btn.setIcon(QIcon(pixmap))
+        self.color_btn.setStyleSheet(f"background-color: {self.current_color.name()};")
+
+    def _load_object_classes(self):
+        """Load object classes from database and populate combo box."""
+        try:
+            classes = self.db_manager.get_object_classes()
+
+            # Clear and repopulate combo box
+            self.class_combo.clear()
+            self.class_combo.addItem("-- Select Class / Show All --", None)
+
+            for cls in classes:
+                self.class_combo.addItem(cls["class_name"], cls)
+
+            logger.debug(f"Loaded {len(classes)} object classes")
+
+        except Exception as e:
+            logger.error(f"Error loading object classes: {e}")
+            QMessageBox.warning(
+                self, "Error", f"Failed to load object classes:\n{str(e)}"
+            )
+
+    def _on_polyline_toggle(self, checked: bool):
+        """Handle polyline tool enable/disable."""
+        self.polyline_enabled = checked
+
+        if checked:
+            self.polyline_toggle_btn.setText("Stop Drawing Polyline")
+            self.polyline_toggle_btn.setStyleSheet(
+                "QPushButton { background-color: #4CAF50; }"
+            )
+        else:
+            self.polyline_toggle_btn.setText("Start Drawing Polyline")
+            self.polyline_toggle_btn.setStyleSheet("")
+
+        self.polyline_enabled_changed.emit(self.polyline_enabled)
+        logger.debug(f"Polyline tool {'enabled' if checked else 'disabled'}")
+
+    def _on_polyline_pen_width_changed(self, width: int):
+        """Handle polyline pen width changes."""
+        self.polyline_pen_width_changed.emit(width)
+        logger.debug(f"Polyline pen width changed to {width}")
+
+    def _on_simplify_toggle(self, state: int):
+        """Handle simplify-on-finish checkbox toggle."""
+        enabled = bool(state)
+        self.simplify_on_finish_changed.emit(enabled)
+        logger.debug(f"Simplify on finish set to {enabled}")
+
+    def _on_eps_change(self, val: float):
+        """Handle epsilon (RDP tolerance) value changes."""
+        epsilon = float(val)
+        self.simplify_epsilon_changed.emit(epsilon)
+        logger.debug(f"Simplification epsilon changed to {epsilon}")
+
+    def _on_show_bboxes_toggle(self, state: int):
+        """Handle 'Show bounding boxes' checkbox toggle."""
+        show = bool(state)
+        self.show_bboxes_changed.emit(show)
+        logger.debug(f"Show bounding boxes set to {show}")
+
+    def _on_color_picker(self):
+        """Open color picker dialog and update the selected object's class color."""
+        if not self.current_class:
+            QMessageBox.warning(
+                self,
+                "No Class Selected",
+                "Please select an object class before changing its color.",
+            )
+            return
+
+        # Use current class color (without alpha) as the base
+        base_color = QColor(self.current_class.get("color", self.current_color.name()))
+        color = QColorDialog.getColor(
+            base_color,
+            self,
+            "Select Class Color",
+            QColorDialog.ShowAlphaChannel,  # Allow alpha in UI, but store RGB in DB
+        )
+
+        if not color.isValid():
+            return
+
+        # Normalize to opaque RGB for storage
+        new_color = QColor(color)
+        new_color.setAlpha(255)
+        hex_color = new_color.name()
+
+        try:
+            # Update in database
+            self.db_manager.update_object_class(
+                class_id=self.current_class["id"], color=hex_color
+            )
+        except Exception as e:
+            logger.error(f"Failed to update class color in database: {e}")
+            QMessageBox.critical(
+                self,
+                "Error",
+                f"Failed to update class color in database:\n{str(e)}",
+            )
+            return
+
+        # Update local class data and combo box item data
+        self.current_class["color"] = hex_color
+        current_index = self.class_combo.currentIndex()
+        if current_index >= 0:
+            self.class_combo.setItemData(current_index, dict(self.current_class))
+
+        # Update info label text
+        info_text = f"Class: {self.current_class['class_name']}\nColor: {hex_color}"
+        if self.current_class.get("description"):
+            info_text += f"\nDescription: {self.current_class['description']}"
+        self.class_info_label.setText(info_text)
+
+        # Use semi-transparent version for polyline pen / button preview
+        class_color = QColor(hex_color)
+        class_color.setAlpha(128)
+        self.current_color = class_color
+        self._update_color_button()
+        self.polyline_pen_color_changed.emit(class_color)
+
+        logger.debug(
+            f"Updated class '{self.current_class['class_name']}' color to "
+            f"{hex_color} (polyline pen alpha={class_color.alpha()})"
+        )
+
+        # Notify listeners (e.g., AnnotationTab) so they can reload/redraw
+        self.class_color_changed.emit()
+
+    def _on_class_selected(self, index: int):
+        """Handle object class selection (including '-- Select Class --')."""
+        class_data = self.class_combo.currentData()
+
+        if class_data:
+            self.current_class = class_data
+
+            # Update info label
+            info_text = (
+                f"Class: {class_data['class_name']}\n" f"Color: {class_data['color']}"
+            )
+            if class_data.get("description"):
+                info_text += f"\nDescription: {class_data['description']}"
+
+            self.class_info_label.setText(info_text)
+
+            # Update polyline pen color to match class color with semi-transparency
+            class_color = QColor(class_data["color"])
+            if class_color.isValid():
+                # Add 50% alpha for semi-transparency
+                class_color.setAlpha(128)
+                self.current_color = class_color
+                self._update_color_button()
+                self.polyline_pen_color_changed.emit(class_color)
+
+            self.class_selected.emit(class_data)
+            logger.debug(f"Selected class: {class_data['class_name']}")
+        else:
+            # "-- Select Class --" chosen: clear current class and show all annotations
+            self.current_class = None
+            self.class_info_label.setText("No class selected")
+            self.class_selected.emit(None)
+            logger.debug("Class selection cleared: showing annotations for all classes")
+
+    def _on_add_class(self):
+        """Handle adding a new object class."""
+        # Get class name
+        class_name, ok = QInputDialog.getText(
+            self, "Add Object Class", "Enter class name:"
+        )
+
+        if not ok or not class_name.strip():
+            return
+
+        class_name = class_name.strip()
+
+        # Check if class already exists
+        existing = self.db_manager.get_object_class_by_name(class_name)
+        if existing:
+            QMessageBox.warning(
+                self, "Class Exists", f"A class named '{class_name}' already exists."
+            )
+            return
+
+        # Get color
+        color = QColorDialog.getColor(self.current_color, self, "Select Class Color")
+
+        if not color.isValid():
+            return
+
+        # Get optional description
+        description, ok = QInputDialog.getText(
+            self, "Class Description", "Enter class description (optional):"
+        )
+
+        if not ok:
+            description = None
+
+        # Add to database
+        try:
+            class_id = self.db_manager.add_object_class(
+                class_name, color.name(), description.strip() if description else None
+            )
+
+            logger.info(f"Added new object class: {class_name} (ID: {class_id})")
+
+            # Reload classes and select the new one
+            self._load_object_classes()
+
+            # Find and select the newly added class
+            for i in range(self.class_combo.count()):
+                class_data = self.class_combo.itemData(i)
+                if class_data and class_data.get("id") == class_id:
+                    self.class_combo.setCurrentIndex(i)
+                    break
+
+            QMessageBox.information(
+                self, "Success", f"Class '{class_name}' added successfully!"
+            )
+
+        except Exception as e:
+            logger.error(f"Error adding object class: {e}")
+            QMessageBox.critical(
+                self, "Error", f"Failed to add object class:\n{str(e)}"
+            )
+
+    def _on_clear_annotations(self):
+        """Handle clear annotations button."""
+        reply = QMessageBox.question(
+            self,
+            "Clear Annotations",
+            "Are you sure you want to clear all annotations?",
+            QMessageBox.Yes | QMessageBox.No,
+            QMessageBox.No,
+        )
+
+        if reply == QMessageBox.Yes:
+            self.clear_annotations_requested.emit()
+            logger.debug("Clear annotations requested")
+
+    def _on_delete_selected_annotation(self):
+        """Handle delete selected annotation button."""
+        self.delete_selected_annotation_requested.emit()
+        logger.debug("Delete selected annotation requested")
+
+    def set_has_selected_annotation(self, has_selection: bool):
+        """
+        Enable/disable actions that require a selected annotation.
+
+        Args:
+            has_selection: True if an annotation is currently selected on the canvas.
+        """
+        self.delete_selected_btn.setEnabled(bool(has_selection))
+
+    def get_current_class(self) -> Optional[Dict]:
+        """Get currently selected object class."""
+        return self.current_class
+
+    def get_polyline_pen_color(self) -> QColor:
+        """Get current polyline pen color."""
+        return self.current_color
+
+    def get_polyline_pen_width(self) -> int:
+        """Get current polyline pen width."""
+        return self.polyline_pen_width_spin.value()
+
+    def is_polyline_enabled(self) -> bool:
+        """Check if polyline tool is enabled."""
+        return self.polyline_enabled
--- a/src/gui/widgets/image_display_widget.py
+++ b/src/gui/widgets/image_display_widget.py
@@ -0,0 +1,282 @@
+"""
+Image display widget with zoom functionality for the microscopy object detection application.
+Reusable widget for displaying images with zoom controls.
+"""
+
+from PySide6.QtWidgets import QWidget, QVBoxLayout, QLabel, QScrollArea
+from PySide6.QtGui import QPixmap, QImage, QKeyEvent
+from PySide6.QtCore import Qt, QEvent, Signal
+from pathlib import Path
+import numpy as np
+
+from src.utils.image import Image, ImageLoadError
+from src.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class ImageDisplayWidget(QWidget):
+    """
+    Reusable widget for displaying images with zoom functionality.
+
+    Features:
+    - Display images from Image objects
+    - Zoom in/out with mouse wheel
+    - Zoom in/out with +/- keyboard keys
+    - Reset zoom with Ctrl+0
+    - Scroll area for large images
+
+    Signals:
+        zoom_changed: Emitted when zoom level changes (float zoom_scale)
+    """
+
+    zoom_changed = Signal(float)  # Emitted when zoom level changes
+
+    def __init__(self, parent=None):
+        """
+        Initialize the image display widget.
+
+        Args:
+            parent: Parent widget
+        """
+        super().__init__(parent)
+
+        self.current_image = None
+        self.original_pixmap = None  # Store original pixmap for zoom
+        self.zoom_scale = 1.0  # Current zoom scale
+        self.zoom_min = 0.1  # Minimum zoom (10%)
+        self.zoom_max = 10.0  # Maximum zoom (1000%)
+        self.zoom_step = 0.1  # Zoom step for +/- keys
+        self.zoom_wheel_step = 0.15  # Zoom step for mouse wheel
+
+        self._setup_ui()
+
+    def _setup_ui(self):
+        """Setup user interface."""
+        layout = QVBoxLayout()
+        layout.setContentsMargins(0, 0, 0, 0)
+
+        # Scroll area for image
+        self.scroll_area = QScrollArea()
+        self.scroll_area.setWidgetResizable(True)
+        self.scroll_area.setMinimumHeight(400)
+
+        self.image_label = QLabel("No image loaded")
+        self.image_label.setAlignment(Qt.AlignCenter)
+        self.image_label.setStyleSheet(
+            "QLabel { background-color: #2b2b2b; color: #888; }"
+        )
+        self.image_label.setScaledContents(False)
+
+        # Enable mouse tracking for wheel events
+        self.image_label.setMouseTracking(True)
+        self.scroll_area.setWidget(self.image_label)
+
+        # Install event filter to capture wheel events on scroll area
+        self.scroll_area.viewport().installEventFilter(self)
+
+        layout.addWidget(self.scroll_area)
+        self.setLayout(layout)
+
+        # Set focus policy to receive keyboard events
+        self.setFocusPolicy(Qt.StrongFocus)
+
+    def load_image(self, image: Image):
+        """
+        Load and display an image.
+
+        Args:
+            image: Image object to display
+
+        Raises:
+            ImageLoadError: If image cannot be displayed
+        """
+        self.current_image = image
+
+        # Reset zoom when loading new image
+        self.zoom_scale = 1.0
+
+        # Convert to QPixmap and display
+        self._display_image()
+
+        logger.debug(f"Loaded image into display widget: {image.width}x{image.height}")
+
+    def clear(self):
+        """Clear the displayed image."""
+        self.current_image = None
+        self.original_pixmap = None
+        self.zoom_scale = 1.0
+        self.image_label.setText("No image loaded")
+        self.image_label.setPixmap(QPixmap())
+        logger.debug("Cleared image display")
+
+    def _display_image(self):
+        """Display the current image in the image label."""
+        if self.current_image is None:
+            return
+
+        try:
+            # Get RGB image data
+            if self.current_image.channels == 3:
+                image_data = self.current_image.get_rgb()
+                height, width, channels = image_data.shape
+            else:
+                image_data = self.current_image.get_grayscale()
+                height, width = image_data.shape
+                channels = 1
+
+            # Ensure data is contiguous for proper QImage display
+            image_data = np.ascontiguousarray(image_data)
+
+            # Use actual stride from numpy array for correct display
+            bytes_per_line = image_data.strides[0]
+
+            qimage = QImage(
+                image_data.data,
+                width,
+                height,
+                bytes_per_line,
+                self.current_image.qtimage_format,
+            )
+
+            # Convert to pixmap
+            pixmap = QPixmap.fromImage(qimage)
+
+            # Store original pixmap for zooming
+            self.original_pixmap = pixmap
+
+            # Apply zoom and display
+            self._apply_zoom()
+
+        except Exception as e:
+            logger.error(f"Error displaying image: {e}")
+            raise ImageLoadError(f"Failed to display image: {str(e)}")
+
+    def _apply_zoom(self):
+        """Apply current zoom level to the displayed image."""
+        if self.original_pixmap is None:
+            return
+
+        # Calculate scaled size
+        scaled_width = int(self.original_pixmap.width() * self.zoom_scale)
+        scaled_height = int(self.original_pixmap.height() * self.zoom_scale)
+
+        # Scale pixmap
+        scaled_pixmap = self.original_pixmap.scaled(
+            scaled_width,
+            scaled_height,
+            Qt.KeepAspectRatio,
+            (
+                Qt.SmoothTransformation
+                if self.zoom_scale >= 1.0
+                else Qt.FastTransformation
+            ),
+        )
+
+        # Display in label
+        self.image_label.setPixmap(scaled_pixmap)
+        self.image_label.setScaledContents(False)
+        self.image_label.adjustSize()
+
+        # Emit zoom changed signal
+        self.zoom_changed.emit(self.zoom_scale)
+
+    def zoom_in(self):
+        """Zoom in on the image."""
+        if self.original_pixmap is None:
+            return
+
+        new_scale = self.zoom_scale + self.zoom_step
+        if new_scale <= self.zoom_max:
+            self.zoom_scale = new_scale
+            self._apply_zoom()
+            logger.debug(f"Zoomed in to {int(self.zoom_scale * 100)}%")
+
+    def zoom_out(self):
+        """Zoom out from the image."""
+        if self.original_pixmap is None:
+            return
+
+        new_scale = self.zoom_scale - self.zoom_step
+        if new_scale >= self.zoom_min:
+            self.zoom_scale = new_scale
+            self._apply_zoom()
+            logger.debug(f"Zoomed out to {int(self.zoom_scale * 100)}%")
+
+    def reset_zoom(self):
+        """Reset zoom to 100%."""
+        if self.original_pixmap is None:
+            return
+
+        self.zoom_scale = 1.0
+        self._apply_zoom()
+        logger.debug("Reset zoom to 100%")
+
+    def set_zoom(self, scale: float):
+        """
+        Set zoom to a specific scale.
+
+        Args:
+            scale: Zoom scale (1.0 = 100%)
+        """
+        if self.original_pixmap is None:
+            return
+
+        # Clamp to min/max
+        scale = max(self.zoom_min, min(self.zoom_max, scale))
+
+        self.zoom_scale = scale
+        self._apply_zoom()
+        logger.debug(f"Set zoom to {int(self.zoom_scale * 100)}%")
+
+    def get_zoom_percentage(self) -> int:
+        """
+        Get current zoom level as percentage.
+
+        Returns:
+            Zoom level as integer percentage (e.g., 100 for 100%)
+        """
+        return int(self.zoom_scale * 100)
+
+    def keyPressEvent(self, event: QKeyEvent):
+        """Handle keyboard events for zooming."""
+        if event.key() in (Qt.Key_Plus, Qt.Key_Equal):
+            # + or = key (= is the unshifted + on many keyboards)
+            self.zoom_in()
+            event.accept()
+        elif event.key() == Qt.Key_Minus:
+            # - key
+            self.zoom_out()
+            event.accept()
+        elif event.key() == Qt.Key_0 and event.modifiers() == Qt.ControlModifier:
+            # Ctrl+0 to reset zoom
+            self.reset_zoom()
+            event.accept()
+        else:
+            super().keyPressEvent(event)
+
+    def eventFilter(self, obj, event: QEvent) -> bool:
+        """Event filter to capture wheel events for zooming."""
+        if event.type() == QEvent.Wheel:
+            wheel_event = event
+            if self.original_pixmap is not None:
+                # Get wheel angle delta
+                delta = wheel_event.angleDelta().y()
+
+                # Zoom in/out based on wheel direction
+                if delta > 0:
+                    # Scroll up = zoom in
+                    new_scale = self.zoom_scale + self.zoom_wheel_step
+                    if new_scale <= self.zoom_max:
+                        self.zoom_scale = new_scale
+                        self._apply_zoom()
+                else:
+                    # Scroll down = zoom out
+                    new_scale = self.zoom_scale - self.zoom_wheel_step
+                    if new_scale >= self.zoom_min:
+                        self.zoom_scale = new_scale
+                        self._apply_zoom()
+
+                return True  # Event handled
+
+        return super().eventFilter(obj, event)
--- a/src/gui_launcher.py
+++ b/src/gui_launcher.py
@@ -0,0 +1,49 @@
+"""GUI launcher module for microscopy object detection application."""
+
+import sys
+from pathlib import Path
+
+from PySide6.QtWidgets import QApplication
+from PySide6.QtCore import Qt
+
+from src import __version__
+from src.gui.main_window import MainWindow
+from src.utils.logger import setup_logging
+from src.utils.config_manager import ConfigManager
+
+
+def main():
+    """Launch the GUI application."""
+    # Setup logging
+    config_manager = ConfigManager()
+    log_config = config_manager.get_section("logging")
+    setup_logging(
+        log_file=log_config.get("file", "logs/app.log"),
+        level=log_config.get("level", "INFO"),
+        log_format=log_config.get("format"),
+    )
+
+    # Enable High DPI scaling
+    QApplication.setHighDpiScaleFactorRoundingPolicy(
+        Qt.HighDpiScaleFactorRoundingPolicy.PassThrough
+    )
+
+    # Create Qt application
+    app = QApplication(sys.argv)
+    app.setApplicationName("Microscopy Object Detection")
+    app.setOrganizationName("MicroscopyLab")
+    app.setApplicationVersion(__version__)
+
+    # Set application style
+    app.setStyle("Fusion")
+
+    # Create and show main window
+    window = MainWindow()
+    window.show()
+
+    # Run application
+    sys.exit(app.exec())
+
+
+if __name__ == "__main__":
+    main()
--- a/src/model/inference.py
+++ b/src/model/inference.py
@@ -87,6 +87,7 @@ class InferenceEngine:
                        "class_name": det["class_name"],
                        "bbox": tuple(bbox_normalized),
                        "confidence": det["confidence"],
+                        "segmentation_mask": det.get("segmentation_mask"),
                        "metadata": {"class_id": det["class_id"]},
                    }
                    detection_records.append(record)
@@ -160,6 +161,7 @@ class InferenceEngine:
        conf: float = 0.25,
        bbox_thickness: int = 2,
        bbox_colors: Optional[Dict[str, str]] = None,
+        draw_masks: bool = True,
    ) -> tuple:
        """
        Detect objects and return annotated image.
@@ -169,6 +171,7 @@ class InferenceEngine:
            conf: Confidence threshold
            bbox_thickness: Thickness of bounding boxes
            bbox_colors: Dictionary mapping class names to hex colors
+            draw_masks: Whether to draw segmentation masks (if available)

        Returns:
            Tuple of (detections, annotated_image_array)
@@ -189,12 +192,8 @@ class InferenceEngine:
                bbox_colors = {}
            default_color = self._hex_to_bgr(bbox_colors.get("default", "#00FF00"))

-            # Draw bounding boxes
+            # Draw detections
            for det in detections:
-                # Get absolute coordinates
-                bbox_abs = det["bbox_absolute"]
-                x1, y1, x2, y2 = [int(v) for v in bbox_abs]
-
                # Get color for this class
                class_name = det["class_name"]
                color_hex = bbox_colors.get(
@@ -202,7 +201,33 @@ class InferenceEngine:
                )
                color = self._hex_to_bgr(color_hex)

-                # Draw box
+                # Draw segmentation mask if available and requested
+                if draw_masks and det.get("segmentation_mask"):
+                    mask_normalized = det["segmentation_mask"]
+                    if mask_normalized and len(mask_normalized) > 0:
+                        # Convert normalized coordinates to absolute pixels
+                        mask_points = np.array(
+                            [
+                                [int(pt[0] * width), int(pt[1] * height)]
+                                for pt in mask_normalized
+                            ],
+                            dtype=np.int32,
+                        )
+
+                        # Create a semi-transparent overlay
+                        overlay = img.copy()
+                        cv2.fillPoly(overlay, [mask_points], color)
+                        # Blend with original image (30% opacity)
+                        cv2.addWeighted(overlay, 0.3, img, 0.7, 0, img)
+
+                        # Draw mask contour
+                        cv2.polylines(img, [mask_points], True, color, bbox_thickness)
+
+                # Get absolute coordinates for bounding box
+                bbox_abs = det["bbox_absolute"]
+                x1, y1, x2, y2 = [int(v) for v in bbox_abs]
+
+                # Draw bounding box
                cv2.rectangle(img, (x1, y1), (x2, y2), color, bbox_thickness)

                # Prepare label
--- a/src/model/yolo_wrapper.py
+++ b/src/model/yolo_wrapper.py
@@ -16,7 +16,7 @@ logger = get_logger(__name__)
 class YOLOWrapper:
    """Wrapper for YOLOv8 model operations."""

-    def __init__(self, model_path: str = "yolov8s.pt"):
+    def __init__(self, model_path: str = "yolov8s-seg.pt"):
        """
        Initialize YOLO model.

@@ -282,6 +282,10 @@ class YOLOWrapper:
                boxes = result.boxes
                image_path = str(result.path)
                orig_shape = result.orig_shape  # (height, width)
+                height, width = orig_shape
+
+                # Check if this is a segmentation model with masks
+                has_masks = hasattr(result, "masks") and result.masks is not None

                for i in range(len(boxes)):
                    # Get normalized coordinates
@@ -299,6 +303,33 @@ class YOLOWrapper:
                            float(v) for v in boxes.xyxy[i].cpu().numpy()
                        ],  # Absolute pixels
                    }
+
+                    # Extract segmentation mask if available
+                    if has_masks:
+                        try:
+                            # Get the mask for this detection
+                            mask_data = result.masks.xy[
+                                i
+                            ]  # Polygon coordinates in absolute pixels
+
+                            # Convert to normalized coordinates
+                            if len(mask_data) > 0:
+                                mask_normalized = []
+                                for point in mask_data:
+                                    x_norm = float(point[0]) / width
+                                    y_norm = float(point[1]) / height
+                                    mask_normalized.append([x_norm, y_norm])
+                                detection["segmentation_mask"] = mask_normalized
+                            else:
+                                detection["segmentation_mask"] = None
+                        except Exception as mask_error:
+                            logger.warning(
+                                f"Error extracting mask for detection {i}: {mask_error}"
+                            )
+                            detection["segmentation_mask"] = None
+                    else:
+                        detection["segmentation_mask"] = None
+
                    detections.append(detection)

            return detections
--- a/src/utils/init.py
+++ b/src/utils/init.py
@@ -0,0 +1,7 @@
+"""
+Utility modules for the microscopy object detection application.
+"""
+
+from src.utils.image import Image, ImageLoadError
+
+__all__ = ["Image", "ImageLoadError"]
--- a/src/utils/config_manager.py
+++ b/src/utils/config_manager.py
@@ -56,7 +56,7 @@ class ConfigManager:
                ],
            },
            "models": {
-                "default_base_model": "yolov8s.pt",
+                "default_base_model": "yolov8s-seg.pt",
                "models_directory": "data/models",
            },
            "training": {
--- a/src/utils/image.py
+++ b/src/utils/image.py
@@ -0,0 +1,291 @@
+"""
+Image loading and management utilities for the microscopy object detection application.
+"""
+
+import cv2
+import numpy as np
+from pathlib import Path
+from typing import Optional, Tuple, Union
+from PIL import Image as PILImage
+
+from src.utils.logger import get_logger
+from src.utils.file_utils import validate_file_path, is_image_file
+
+from PySide6.QtGui import QImage
+
+logger = get_logger(__name__)
+
+
+class ImageLoadError(Exception):
+    """Exception raised when an image cannot be loaded."""
+
+    pass
+
+
+class Image:
+    """
+    A class for loading and managing images from file paths.
+
+    Supports multiple image formats: .jpg, .jpeg, .png, .tif, .tiff, .bmp
+    Provides access to image data in multiple formats (OpenCV/numpy, PIL).
+
+    Attributes:
+        path: Path to the image file
+        data: Image data as numpy array (OpenCV format, BGR)
+        pil_image: Image data as PIL Image (RGB)
+        width: Image width in pixels
+        height: Image height in pixels
+        channels: Number of color channels
+        format: Image file format
+        size_bytes: File size in bytes
+    """
+
+    SUPPORTED_EXTENSIONS = [".jpg", ".jpeg", ".png", ".tif", ".tiff", ".bmp"]
+
+    def __init__(self, image_path: Union[str, Path]):
+        """
+        Initialize an Image object by loading from a file path.
+
+        Args:
+            image_path: Path to the image file (string or Path object)
+
+        Raises:
+            ImageLoadError: If the image cannot be loaded or is invalid
+        """
+        self.path = Path(image_path)
+        self._data: Optional[np.ndarray] = None
+        self._pil_image: Optional[PILImage.Image] = None
+        self._width: int = 0
+        self._height: int = 0
+        self._channels: int = 0
+        self._format: str = ""
+        self._size_bytes: int = 0
+        self._dtype: Optional[np.dtype] = None
+
+        # Load the image
+        self._load()
+
+    def _load(self) -> None:
+        """
+        Load the image from disk.
+
+        Raises:
+            ImageLoadError: If the image cannot be loaded
+        """
+        # Validate path
+        if not validate_file_path(str(self.path), must_exist=True):
+            raise ImageLoadError(f"Invalid or non-existent file path: {self.path}")
+
+        # Check file extension
+        if not is_image_file(str(self.path), self.SUPPORTED_EXTENSIONS):
+            ext = self.path.suffix.lower()
+            raise ImageLoadError(
+                f"Unsupported image format: {ext}. "
+                f"Supported formats: {', '.join(self.SUPPORTED_EXTENSIONS)}"
+            )
+
+        try:
+            # Load with OpenCV (returns BGR format)
+            self._data = cv2.imread(str(self.path), cv2.IMREAD_UNCHANGED)
+
+            if self._data is None:
+                raise ImageLoadError(f"Failed to load image with OpenCV: {self.path}")
+
+            # Extract metadata
+            self._height, self._width = self._data.shape[:2]
+            self._channels = self._data.shape[2] if len(self._data.shape) == 3 else 1
+            self._format = self.path.suffix.lower().lstrip(".")
+            self._size_bytes = self.path.stat().st_size
+            self._dtype = self._data.dtype
+
+            # Load PIL version for compatibility (convert BGR to RGB)
+            if self._channels == 3:
+                rgb_data = cv2.cvtColor(self._data, cv2.COLOR_BGR2RGB)
+                self._pil_image = PILImage.fromarray(rgb_data)
+            elif self._channels == 4:
+                rgba_data = cv2.cvtColor(self._data, cv2.COLOR_BGRA2RGBA)
+                self._pil_image = PILImage.fromarray(rgba_data)
+            else:
+                # Grayscale
+                self._pil_image = PILImage.fromarray(self._data)
+
+            logger.info(
+                f"Successfully loaded image: {self.path.name} "
+                f"({self._width}x{self._height}, {self._channels} channels, "
+                f"{self._format.upper()})"
+            )
+
+        except Exception as e:
+            logger.error(f"Error loading image {self.path}: {e}")
+            raise ImageLoadError(f"Failed to load image: {e}") from e
+
+    @property
+    def data(self) -> np.ndarray:
+        """
+        Get image data as numpy array (OpenCV format, BGR or grayscale).
+
+        Returns:
+            Image data as numpy array
+        """
+        if self._data is None:
+            raise ImageLoadError("Image data not available")
+        return self._data
+
+    @property
+    def pil_image(self) -> PILImage.Image:
+        """
+        Get image data as PIL Image (RGB or grayscale).
+
+        Returns:
+            PIL Image object
+        """
+        if self._pil_image is None:
+            raise ImageLoadError("PIL image not available")
+        return self._pil_image
+
+    @property
+    def width(self) -> int:
+        """Get image width in pixels."""
+        return self._width
+
+    @property
+    def height(self) -> int:
+        """Get image height in pixels."""
+        return self._height
+
+    @property
+    def shape(self) -> Tuple[int, int, int]:
+        """
+        Get image shape as (height, width, channels).
+
+        Returns:
+            Tuple of (height, width, channels)
+        """
+        print("shape", self._height, self._width, self._channels)
+        return (self._height, self._width, self._channels)
+
+    @property
+    def channels(self) -> int:
+        """Get number of color channels."""
+        return self._channels
+
+    @property
+    def format(self) -> str:
+        """Get image file format (e.g., 'jpg', 'png')."""
+        return self._format
+
+    @property
+    def size_bytes(self) -> int:
+        """Get file size in bytes."""
+        return self._size_bytes
+
+    @property
+    def size_mb(self) -> float:
+        """Get file size in megabytes."""
+        return self._size_bytes / (1024 * 1024)
+
+    @property
+    def dtype(self) -> np.dtype:
+        """Get the data type of the image array."""
+        if self._dtype is None:
+            raise ImageLoadError("Image dtype not available")
+        return self._dtype
+
+    @property
+    def qtimage_format(self) -> QImage.Format:
+        """
+        Get the appropriate QImage format for the image.
+
+        Returns:
+            QImage.Format enum value
+        """
+        if self._channels == 3:
+            return QImage.Format_RGB888
+        elif self._channels == 4:
+            return QImage.Format_RGBA8888
+        elif self._channels == 1:
+            if self._dtype == np.uint16:
+                return QImage.Format_Grayscale16
+            else:
+                return QImage.Format_Grayscale8
+        else:
+            raise ImageLoadError(f"Unsupported number of channels: {self._channels}")
+
+    def get_rgb(self) -> np.ndarray:
+        """
+        Get image data as RGB numpy array.
+
+        Returns:
+            Image data in RGB format as numpy array
+        """
+        if self._channels == 3:
+            return cv2.cvtColor(self._data, cv2.COLOR_BGR2RGB)
+        elif self._channels == 4:
+            return cv2.cvtColor(self._data, cv2.COLOR_BGRA2RGBA)
+        else:
+            return self._data
+
+    def get_grayscale(self) -> np.ndarray:
+        """
+        Get image as grayscale numpy array.
+
+        Returns:
+            Grayscale image as numpy array
+        """
+        if self._channels == 1:
+            return self._data
+        else:
+            return cv2.cvtColor(self._data, cv2.COLOR_BGR2GRAY)
+
+    def copy(self) -> np.ndarray:
+        """
+        Get a copy of the image data.
+
+        Returns:
+            Copy of image data as numpy array
+        """
+        return self._data.copy()
+
+    def resize(self, width: int, height: int) -> np.ndarray:
+        """
+        Resize the image to specified dimensions.
+
+        Args:
+            width: Target width in pixels
+            height: Target height in pixels
+
+        Returns:
+            Resized image as numpy array (does not modify original)
+        """
+        return cv2.resize(self._data, (width, height))
+
+    def is_grayscale(self) -> bool:
+        """
+        Check if image is grayscale.
+
+        Returns:
+            True if image is grayscale (1 channel)
+        """
+        return self._channels == 1
+
+    def is_color(self) -> bool:
+        """
+        Check if image is color.
+
+        Returns:
+            True if image has 3 or more channels
+        """
+        return self._channels >= 3
+
+    def __repr__(self) -> str:
+        """String representation of the Image object."""
+        return (
+            f"Image(path='{self.path.name}', "
+            f"shape=({self._width}x{self._height}x{self._channels}), "
+            f"format={self._format}, "
+            f"size={self.size_mb:.2f}MB)"
+        )
+
+    def __str__(self) -> str:
+        """String representation of the Image object."""
+        return self.__repr__()
--- a/tests/test_image.py
+++ b/tests/test_image.py
@@ -0,0 +1,145 @@
+"""
+Tests for the Image class.
+"""
+
+import pytest
+import numpy as np
+from pathlib import Path
+from src.utils.image import Image, ImageLoadError
+
+
+class TestImage:
+    """Test cases for the Image class."""
+
+    def test_load_nonexistent_file(self):
+        """Test loading a non-existent file raises ImageLoadError."""
+        with pytest.raises(ImageLoadError):
+            Image("nonexistent_file.jpg")
+
+    def test_load_unsupported_format(self, tmp_path):
+        """Test loading an unsupported format raises ImageLoadError."""
+        # Create a dummy file with unsupported extension
+        test_file = tmp_path / "test.txt"
+        test_file.write_text("not an image")
+
+        with pytest.raises(ImageLoadError):
+            Image(test_file)
+
+    def test_supported_extensions(self):
+        """Test that supported extensions are correctly defined."""
+        expected_extensions = [".jpg", ".jpeg", ".png", ".tif", ".tiff", ".bmp"]
+        assert Image.SUPPORTED_EXTENSIONS == expected_extensions
+
+    def test_image_properties(self, tmp_path):
+        """Test image properties after loading."""
+        # Create a simple test image using numpy and cv2
+        import cv2
+
+        test_img = np.zeros((100, 200, 3), dtype=np.uint8)
+        test_img[:, :] = [255, 0, 0]  # Blue in BGR
+
+        test_file = tmp_path / "test.jpg"
+        cv2.imwrite(str(test_file), test_img)
+
+        # Load the image
+        img = Image(test_file)
+
+        # Check properties
+        assert img.width == 200
+        assert img.height == 100
+        assert img.channels == 3
+        assert img.format == "jpg"
+        assert img.shape == (100, 200, 3)
+        assert img.size_bytes > 0
+        assert img.is_color()
+        assert not img.is_grayscale()
+
+    def test_get_rgb(self, tmp_path):
+        """Test RGB conversion."""
+        import cv2
+
+        # Create BGR image
+        test_img = np.zeros((50, 50, 3), dtype=np.uint8)
+        test_img[:, :] = [255, 0, 0]  # Blue in BGR
+
+        test_file = tmp_path / "test_rgb.png"
+        cv2.imwrite(str(test_file), test_img)
+
+        img = Image(test_file)
+        rgb_data = img.get_rgb()
+
+        # RGB should have red channel at 255
+        assert rgb_data[0, 0, 0] == 0  # R
+        assert rgb_data[0, 0, 1] == 0  # G
+        assert rgb_data[0, 0, 2] == 255  # B (was BGR blue)
+
+    def test_get_grayscale(self, tmp_path):
+        """Test grayscale conversion."""
+        import cv2
+
+        test_img = np.zeros((50, 50, 3), dtype=np.uint8)
+        test_img[:, :] = [128, 128, 128]
+
+        test_file = tmp_path / "test_gray.png"
+        cv2.imwrite(str(test_file), test_img)
+
+        img = Image(test_file)
+        gray_data = img.get_grayscale()
+
+        assert len(gray_data.shape) == 2  # Should be 2D
+        assert gray_data.shape == (50, 50)
+
+    def test_copy(self, tmp_path):
+        """Test copying image data."""
+        import cv2
+
+        test_img = np.zeros((50, 50, 3), dtype=np.uint8)
+
+        test_file = tmp_path / "test_copy.png"
+        cv2.imwrite(str(test_file), test_img)
+
+        img = Image(test_file)
+        copied = img.copy()
+
+        # Modify copy
+        copied[0, 0] = [255, 255, 255]
+
+        # Original should be unchanged
+        assert not np.array_equal(img.data[0, 0], copied[0, 0])
+
+    def test_resize(self, tmp_path):
+        """Test image resizing."""
+        import cv2
+
+        test_img = np.zeros((100, 100, 3), dtype=np.uint8)
+
+        test_file = tmp_path / "test_resize.png"
+        cv2.imwrite(str(test_file), test_img)
+
+        img = Image(test_file)
+        resized = img.resize(50, 50)
+
+        assert resized.shape == (50, 50, 3)
+        # Original should be unchanged
+        assert img.width == 100
+        assert img.height == 100
+
+    def test_str_repr(self, tmp_path):
+        """Test string representation."""
+        import cv2
+
+        test_img = np.zeros((100, 200, 3), dtype=np.uint8)
+
+        test_file = tmp_path / "test_str.jpg"
+        cv2.imwrite(str(test_file), test_img)
+
+        img = Image(test_file)
+
+        str_repr = str(img)
+        assert "test_str.jpg" in str_repr
+        assert "100x200x3" in str_repr
+        assert "jpg" in str_repr
+
+        repr_str = repr(img)
+        assert "Image" in repr_str
+        assert "test_str.jpg" in repr_str