Native object detection, instance segmentation, and image classification in Rust with Burn

Performance | Model support

---

Montgomery is an experimental Rust computer-vision stack:

• Computer vision inference on CPU or GPU
• WGPU training with validation, resumable checkpoints, and ready-to-use exports
• Detection, instance segmentation, and classification
• Burnpack and ONNX export

Normal inference needs no Python, PyTorch, or ONNX Runtime.

Supported models

Model	Variants	Tasks
YOLOX	nano, tiny, s, m, l, x	Detect
YOLOv3	tinyu	Detect
YOLOv8	n, s, m, l, x	Detect, segment, classify
YOLOv10	n, s, m, b, l, x	Detect
YOLO11	n, s, m, l, x	Detect, segment, classify
YOLO12	n, s, m, l, x	Detect
YOLO26	n, s, m, l, x	Detect, segment, classify

Rust API

use montgomery::Model;

fn main() -> montgomery::Result<()> {
    let model = Model::new("yolo26n.bpk")?;

    let prediction = model.inference("image.jpg")?;
    for detection in prediction.detections().expect("detection model") {
        println!("{}: {:.1}%", detection.class_name, detection.confidence * 100.0);
    }
    Ok(())
}

Inference

montgomery predict --model best.bpk --source image.jpg --json

Benchmark cold-start and steady-state inference without loading an image:

montgomery bench --device gpu --model best.bpk

Train

# Fresh initialization
montgomery train --architecture yolo26n --data dataset.yaml --epochs 100

# Pretrained initialization
montgomery train --model yolo26n.bpk --data dataset.yaml --epochs 100

# Exact continuation (model and dataset come from the training checkpoint)
montgomery train --resume runs/train/checkpoints/last

Exactly one initialization mode is required: --architecture means scratch, --model requires a pretrained .bpk, and --resume requires a full native training checkpoint. A Burnpack initializes a new run; it is not a resumable optimizer checkpoint.

Every run contains:

• results.csv, results.svg, and validation.jsonl
• exports/best.bpk and exports/last.bpk
• checkpoints/best and checkpoints/last

Only the best and latest resumable models are retained. Use --save-period to control recovery checkpoints and --workers to override automatic CPU worker selection.

Export ONNX

montgomery export-onnx --model yolo26n.bpk

This reads the explicit Burnpack and writes yolo26n.onnx; use --output to select another path.

The offline exporter validates the graph with ONNX Runtime. Setup details are in tools/onnx/README.md.

Develop

Stable Rust is the only requirement to start development

git clone https://github.com/boquila/montgomery.git && cd montgomery
cargo test

The same checks used by CI are:

cargo fmt --check
cargo test
cargo clippy --all-targets -- -D warnings
cargo check --no-default-features --lib

See docs/MODEL_BRINGUP.md for new model families.

License

Montgomery is AGPL-3.0.