Use Cases#

The benchmark provides datasets for several imaging modalities and controlled workloads. Each page describes the source data, forward model, BenchOpt installation and preparation steps, and parameters available in experiment YAML.

Multi-frame Super-resolution

Recover a high-resolution color image from multiple blurred, downsampled, and noisy observations.

Multi-frame Super-resolution
Tomography

Reconstruct 2D slices or 3D volumes from ASTRA-backed parallel-beam and cone-beam projections.

Tomography
Radio Interferometry

Recover a sky image from sparse Fourier measurements simulated for a configurable telescope observation.

Radio Interferometry
Synthetic Workloads

Generate scalable 2D and 3D signals for controlled super-resolution and denoising workloads.

Synthetic Workloads

Installing and Preparing Data#

Installation and preparation are separate operations:

  • benchopt install installs requirements declared by the selected benchmark components. A dataset may also provide a custom installer, as radio interferometry does for its simulation container.

  • benchopt prepare calls the selected dataset’s prepare() method. This is where reusable input files or simulations are downloaded and cached. It does not construct the tensors and operators for every benchmark run; that happens when BenchOpt loads the dataset.

Both commands can select one dataset directly or use an experiment configuration:

benchopt install benchmark_inference/. -d tomography_3d
benchopt prepare benchmark_inference/. -d tomography_3d

benchopt install benchmark_inference/. \
    --config benchmark_inference/configs/examples/tomography_3d.yml
benchopt prepare benchmark_inference/. \
    --config benchmark_inference/configs/examples/tomography_3d.yml

Preparation is cached by BenchOpt. Use benchopt prepare --force when a cached preparation must be repeated.