ApexLabel

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Free and open-source object detection annotation tool with SAM click-to-segment. Train a YOLO model directly in the app using your annotations, then use it to auto-label new images, just review and tweak predictions instead of annotating from scratch. Bootstrap your way to a full dataset in a fraction of the time.

I'm not much of a designer, so I tried to make it look like early 2000s freeware.

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Key Features

• Auto-save — Annotations save automatically when you navigate to a new image. No manual saving required.

• YOLO Bootstrap Training — Train a YOLO model on your annotations, then enable prediction assist. The model pre-labels new images so you just tweak the predictions instead of starting from scratch.

• LLaVA Validation — Optional LLM-powered validation using LLaVA to review annotations and filter false positives. Useful for large datasets where manual review isn't feasible.

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How It Works

Click to Segment

Click on any object and SAM automatically creates a segmentation mask.

Pan and Zoom

Ctrl+Scroll to zoom. Hold Space and drag to pan.

Manual Mode + Delete

Switch to manual mode to draw bounding boxes. Shift+Click to delete annotations.

Undo

Ctrl+Z to undo.

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Tech Stack

Component	Technology
Segmentation	Segment Anything (SAM)
Object Detection	Ultralytics YOLO
Deep Learning	PyTorch
GUI	tkinter
Vision Validation	LLaVA (optional)

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Getting Started

./scripts/install_dependencies.sh
source .venv/bin/activate

# Set your class names
cp config/default_config.yaml config.yaml
# Edit config.yaml → class_names: ["your_object"]

# Launch
python -m sam_annotation --config config.yaml /path/to/images

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Satellite Imagery

Need sample images? Download tiles from ArcGIS World Imagery:

# 100 tiles around Los Angeles
python scripts/download_satellite.py --lat 34.0522 --lon -118.2437 --count 100

# Higher resolution (zoom 19 = ~0.3m/pixel)
python scripts/download_satellite.py --lat 40.7128 --lon -74.0060 -n 50 -z 19

# Custom output
python scripts/download_satellite.py --lat 37.7749 --lon -122.4194 -o data/sf_tiles

Free for non-commercial use. Tiles download in a spiral pattern from center.

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Controls

Input	Action
Click	Segment object at cursor
Shift+Click	Delete annotation
Scroll	Adjust SAM threshold
Ctrl+Scroll	Zoom
Space+Drag	Pan
← →	Previous / Next image
Ctrl+Z	Undo
Ctrl+S	Save

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Training Loop

The bootstrap workflow lets you build a detector with minimal manual effort:

1. Annotate a small batch of images with SAM (~50-100)
2. Export annotations to YOLO format
3. Train a YOLO model (built-in, one click)
4. Enable prediction assist — model pre-labels new images
5. Review and tweak predictions instead of annotating from scratch
6. Repeat — each iteration improves the model

Best model saves to models/current_best_yolo.pt

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Configuration

# config.yaml
class_names: ["car", "truck", "bus"]  # Required

# Optional
default_epochs: 50
default_batch_size: 8
default_image_size: 640

Full options in config/default_config.yaml

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Bootstrap Training (Optional)

For automated validation using LLaVA vision model:

# Install Ollama → https://ollama.ai
ollama pull llava:7b

# Configure prompts in config.yaml, then:
python scripts/run_bootstrap.py --config config.yaml --start-llava

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Requirements

• Python 3.8+
• CUDA GPU with 8GB+ VRAM
• Ollama (optional, for LLaVA bootstrap)

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Project Structure

ApexLabel/
├── sam_annotation/    # Main application
├── bootstrap/         # LLaVA validation pipeline
├── config/            # Configuration system
├── prompts/           # LLaVA prompt templates
├── scripts/           # Setup & utilities
├── data/              # Your images (gitignored)
├── models/            # Trained weights (gitignored)
└── output/            # Exports (gitignored)

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License

LGPL-2.1 — See LICENSE for details.