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name: simpledet description: "Guides SimpleDet MXNet object detection and instance-recognition workflows, including legacy CUDA setup, roidb preparation, configurable training and evaluation, inference benchmarking, and detector customization." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

SimpleDet

Use this repo skill when a task names SimpleDet, detection_train.py, detection_test.py, mask_test.py, detection_infer_speed.py, mxnext, SimpleDet roidb files, or its symbolic detector/configuration APIs.

This is a legacy MXNet 1.x framework. Read the relevant route before proposing commands; do not assume a modern MXNet/PyTorch environment is compatible. Read repo-provenance.md before deciding whether this skill is stale.

Route by task

• Install, import, extension build, CUDA, checkpoints, logs, TensorBoard, or distributed prerequisites: read setup-and-operations/SKILL.md.
• COCO/VOC/CrowdHuman/JSON annotations, roidb generation, or validation: read data-preparation/SKILL.md.
• Train, evaluate, mask-test, benchmark speed, fine-tune, or select a config: read detection-workflows/SKILL.md.
• Compose a detector, add a component, choose a model family, or change symbolic outputs: read model-customization/SKILL.md.

For a task spanning routes, start with setup-and-operations for runtime gates, then data-preparation, then detection-workflows; use model-customization when the architecture or config contract changes.

Runtime contract

SimpleDet is checkout-driven rather than a normal installable package. The public baseline is Python 3.7, NumPy 1.x, OpenCV, pytz, patched pycocotools, mxnext, and a CUDA-enabled MXNet 1.6-era build. The standard train/test/speed entry points construct mx.gpu contexts, so a CPU import is not evidence that the main workflows run.

Before any long command:

1. Run scripts/check_environment.py with --repo-root pointing at the checkout.
2. Inspect a config with detection-workflows/scripts/inspect_config.py.
3. Validate caches with data-preparation/scripts/validate_roidb.py.
4. Use scripts/run_workflow.py with --dry-run to inspect the exact entry point, config, and working directory.
5. Keep datasets, weights, logs, and experiments outside the generated skill.

The production inspection could not obtain the documented legacy CUDA wheel and had no nvcc; CUDA execution is an explicit unresolved gate, not a verified claim. Read troubleshooting.md for the consequence.

Workflow wrapper

The wrapper makes checkout selection explicit and has no install/download/kill behavior. Remove --dry-run only after backend, data, config, and checkpoint checks pass:

python <skill-root>/scripts/run_workflow.py --repo-root /path/to/simpledet \
  --entrypoint speed --config config/faster_r50v1_fpn_1x.py \
  --shape 800 1333 --gpu 0 --count 100 --dry-run

The selected entry point may still allocate GPUs, read large data, load weights, and write under experiments/.

Shared references

• references/troubleshooting.md covers cross-cutting import, data, config, checkpoint, and backend failures.
• references/coverage-notes.md records the integrated scope and intentional omissions.