AdelaiDet

AdelaiDet is an AIM/Adelaide Detectron2-based research platform for instance-level recognition: object detection, instance segmentation, text spotting, keypoint detection, and related deployment utilities. Use this repo skill when a task names AdelaiDet, adet, FCOS, BlendMask, CondInst, BoxInst, SOLOv2, BAText/ABCNet, MEInst, FCPose, DenseCL, or asks how to train, evaluate, demo, prepare data, or export models for this repository.

Start here

• Read references/repo-provenance.md before refreshing the skill or checking whether the source snapshot matches a task.
• Read references/compatibility.md before installing or building AdelaiDet. This repo is legacy Detectron2 code and needs a version-compatible PyTorch/CUDA stack.
• Read references/model-overview.md to choose a config family and understand which workflow owns it.
• Read references/api-reference.md for the verified import surface, config keys, registries, custom ops, and public CLIs.
• Read references/troubleshooting.md when install, import, CUDA extension, CLI, dataset, checkpoint, or export errors appear.

Install and smoke-check

The verified runtime stack is CUDA-capable and legacy-compatible:

• Python 3.9
• PyTorch 1.10.x with CUDA 11.3
• TorchVision 0.11.x
• Detectron2 0.6 built for the same PyTorch/CUDA pair
• AdelaiDet installed editable from a matching source checkout
• Pillow <10, rapidfuzz <3, NumPy 1.23.x, and OpenCV headless 4.8.x

Do not start with a modern PyTorch 2.x stack for unmodified AdelaiDet CUDA extensions: the source includes legacy THC headers in ml_nms.cu that are absent from PyTorch 2.x.

After installation, run the skill-owned smoke check:

python scripts/check_install.py --cuda-ops

Run without --cuda-ops only when you intentionally need a CPU/import-only diagnosis.

Route map

setup-build

Use this route for environment creation, Detectron2/PyTorch/CUDA versioning, editable builds, compiled adet._C checks, custom op smoke tests, and install failure diagnosis.

Read:

• sub-skills/setup-build/SKILL.md
• sub-skills/setup-build/references/setup-build.md
• sub-skills/setup-build/references/runtime-checks.md

train-eval

Use this route for Detectron2-style AdelaiDet training, evaluation, config overrides, model-family selection for training, checkpoints, distributed launches, and output directory expectations.

Read:

• sub-skills/train-eval/SKILL.md
• sub-skills/train-eval/references/train-eval-workflows.md
• sub-skills/train-eval/references/config-selection.md

demo-visualize

Use this route for image/video/webcam demos, VisualizationDemo, confidence thresholds, text/non-text visualizations, and dataset visualization.

Read:

• sub-skills/demo-visualize/SKILL.md
• sub-skills/demo-visualize/references/demo-workflows.md
• sub-skills/demo-visualize/references/visualization.md

text-spotting

Use this route for ABCNet/BAText, BezierAlign, text datasets, custom dictionaries, lexicons, text evaluation, and OCR-specific pitfalls.

Read:

• sub-skills/text-spotting/SKILL.md
• sub-skills/text-spotting/references/text-workflows.md
• sub-skills/text-spotting/references/text-data-and-eval.md

data-prep

Use this route for COCO/PIC/LVIS/text dataset layouts, semantic mask generation, dataset registration, mapper expectations, MEInst mask encoding, and data validation.

Read:

• sub-skills/data-prep/SKILL.md
• sub-skills/data-prep/references/dataset-preparation.md
• sub-skills/data-prep/references/data-formats.md

export-convert

Use this route for checkpoint key conversion, optimizer stripping, FCOS/BlendMask weight migration, ONNX export, and optional Caffe/NCNN/TensorRT deployment caveats.

Read:

• sub-skills/export-convert/SKILL.md
• sub-skills/export-convert/references/export-and-checkpoints.md
• sub-skills/export-convert/references/onnx-export.md

Skill-owned scripts

• scripts/check_install.py — verify import, Detectron2 registries, config keys, and optionally CUDA custom ops.
• Sub-skill scripts wrap or adapt the repository workflows with preflight validation. When a script asks for --repo-root, pass a source checkout matching the provenance baseline or a refreshed AdelaiDet checkout.

Operating cautions

• Full training, evaluation, demos with real images, and ONNX runtime validation need external datasets, model weights, and sometimes extra runtimes. Use help/dry-run checks first.
• ONNX/Caffe/NCNN/TensorRT shell pipelines from the source repository are reference-only here because they assume external workspaces and large artifacts.
• Keep installation/build issues routed to setup-build; do not debug model configs until scripts/check_install.py --cuda-ops passes for CUDA workflows.