---

name: "nanodet" description: "Routes NanoDet users to dataset/config, training/evaluation, and inference/export workflows for the NanoDet object-detection repository." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

NanoDet

NanoDet is a lightweight, config-driven object-detection toolkit. Use this repo skill when you need to inspect configs, prepare datasets, train or evaluate models, run inference, or export models for deployment.

Start here

• Read references/api-reference.md for the verified builders, dataset constructors, training task, and utility APIs.
• Read references/model-overview.md when you need the supported backbone / neck / head / loss combinations.
• Read references/troubleshooting.md when installation, config loading, checkpoint loading, or export fails.
• Read references/repo-provenance.md when you want to check whether this skill matches the current repository snapshot or before refreshing it.

Install and import

The repository was verified with a CPU-only PyTorch stack on Python 3.8. The package name and import root are both nanodet.

1. Install a compatible PyTorch / torchvision pair first.
2. Install the repo requirements and timm.
3. Install the repo in editable mode.

Example:

python -m pip install torch==1.13.1+cpu torchvision==0.14.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
python -m pip install pytorch-lightning<2.0 timm pycocotools onnx onnx-simplifier onnxruntime opencv-python pyaml tensorboard matplotlib imagesize termcolor tqdm pytest
python -m pip install -e .
python -I -c "import nanodet, torch; print(nanodet.__version__, torch.__version__)"

If you want a quick sanity check of the installed package, run scripts/check_install.py from this skill tree.

Route map

dataset-config

Use this route for YAML config inspection, dataset format questions, preprocessing pipelines, and config-driven model assembly.

Read:

• sub-skills/dataset-config/SKILL.md
• sub-skills/dataset-config/references/configuration.md
• sub-skills/dataset-config/references/data-formats.md

Typical tasks:

• Validate a config file before training.
• Check a COCO, XML, or YOLO dataset layout.
• Understand class_names, input_size, keep_ratio, multi_scale, or pipeline settings.

training

Use this route for training, validation, test-time evaluation, checkpoint loading, EMA, logging, optimizer settings, and multiprocessing behavior.

Read:

• sub-skills/training/SKILL.md
• sub-skills/training/references/workflows.md
• sub-skills/training/references/troubleshooting.md

Typical tasks:

• Launch or debug training and validation.
• Resume from a checkpoint or convert an old checkpoint.
• Inspect the Lightning task, logger, evaluator, or optimizer behavior.

inference-export

Use this route for image/video/webcam inference, ONNX export, TorchScript export, RepVGG deploy conversion, FLOPs inspection, and deployment notes.

Read:

• sub-skills/inference-export/SKILL.md
• sub-skills/inference-export/references/workflows.md
• sub-skills/inference-export/references/deployment.md
• sub-skills/inference-export/references/troubleshooting.md

Typical tasks:

• Run a demo on an image, video, or webcam.
• Export a checkpoint for ONNX or TorchScript consumers.
• Prepare RepVGG models for deployment backends.

Shared runtime helpers

• scripts/check_install.py — verify the package import surface and optional runtime dependencies.
• scripts/check_install.py --config <config.yml> — optionally build a model from a config as a quick smoke check.

Notes

• The selected extraction scope is CPU-first because the verified native candidates are CPU-safe.
• CUDA is supported by the repository docs, but it is optional for this generated skill unless a later task explicitly asks for it.
• Keep all runtime links inside this skill tree; do not depend on the original repository checkout at runtime.