SimpleDet Docs

CLI reference

SimpleDet installs one console script and several public Python workflow callables.

Overview Quickstart Core Concepts API Reference

Use this page to see which entry points are real shell commands and which ones are Python callables only.

Installed console script

Command	Description
`simpledet --version`	Print the installed package version
`python -m simpledet --check-runtime`	Verify the optional runtime stack used by the package
`python -m simpledet --list-detectors`	List supported high-level detector architectures
`python -m simpledet --list-encoders`	List supported encoder/backbone names
`python -m simpledet --show-detector-help retinanet`	Explain one detector family and recommended encoders
`python -m simpledet --init-project project.toml`	Write a starter project config
`python -m simpledet --project-validate path/to/project.toml`	Validate a project config without running training
`python -m simpledet --project-run path/to/project.toml --stages build train`	Run selected stages from a project config
`python -m simpledet --train-root /data/project ...`	Run training directly from CLI arguments
`python -m simpledet --infer-root /data/project ...`	Run inference directly from CLI arguments
`python -m simpledet --eval-root /data/project ...`	Run evaluation directly from CLI arguments

Discovery commands

Use these before direct execution if you do not want to guess supported architecture or encoder names.

python -m simpledet --list-detectors
python -m simpledet --list-encoders
python -m simpledet --show-detector-help retinanet

--show-detector-help prints the detector family, a short summary, and a few encoder suggestions for the selected architecture.

Direct non-config workflow

Use these commands when your dataset follows the standard project layout and you want native execution without creating a project file.

python -m simpledet --train-root /data/project \
  --categories car building ship \
  --in-channels 3 \
  --detector retinanet \
  --encoder resnet18.a1_in1k \
  --batch-size 2 \
  --max-epochs 30

python -m simpledet --infer-root /data/project \
  --categories car building ship \
  --in-channels 3 \
  --detector retinanet \
  --encoder resnet18.a1_in1k

python -m simpledet --eval-root /data/project \
  --categories car building ship \
  --in-channels 3 \
  --detector retinanet \
  --encoder resnet18.a1_in1k

Direct execution currently requires --categories, --in-channels, and a high-level detector selection.

python -m simpledet --train-root /data/project \
  --categories car building ship \
  --in-channels 3 \
  --detector retinanet \
  --encoder resnet18.a1_in1k \
  --batch-size 2 \
  --max-epochs 30

Use one of these model-definition paths:

--detector with optional --encoder and --num-classes for suite-backed high-level model selection

Direct CLI execution uses the native Lightning backend automatically when you use one of the supported architectures, for example --detector retinanet, --detector vfnet, --detector centernet, or --detector faster_rcnn.

Optional runtime flags include --tif-channels-to-load, --result-folder, --resize, --batch-size, --max-epochs, --learning-rate, and --no-validate.

Project config workflow

Use a JSON or TOML file when you want a repeatable operational entrypoint for the native runtime.

python -m simpledet --init-project project.toml
python -m simpledet --project-validate project.toml
python -m simpledet --project-run project.toml --stages build test

A reusable example can be created with --init-project and then adjusted for your dataset root and output folder.

`simpledet.train`

Public Python callable, not a shell executable.

train(*, config=None, pipeline=None, build=True, **pipeline_kwargs)

config=... uses the lightweight torchvision path
Forwarded runtime kwargs map to the native execution helpers
detector_spec=... is the supported high-level model input

`simpledet.detect`

detect(*, pipeline=None, build=True, **pipeline_kwargs)

Runs the package inference helper for the current native runtime. This is separate from load_model() plus predict().

`simpledet.evaluate`

evaluate(*, pipeline=None, build=True, **pipeline_kwargs)

Currently a thin wrapper around the same native evaluation path used by run_evaluation(...).

Lightweight inference helpers

load_model(checkpoint, *, device="cpu", model_name=None, num_classes=None, score_threshold=0.05, max_detections=None)
predict(image, *, model=None, score_threshold=None)