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Setup & Annotation Tab

Setup & Annotation is where you define the project structure, label schema, and dataset layout for later training and inference.

At a glance

Best for Typical output Usually next
Project setup, annotation launch, dataset preparation Project scaffold, labels, train/val split, dataset.yaml Model Training

Main workflow

Set project root
  -> Define keypoints / behaviors / skeleton
  -> Annotate
  -> Create train/val split
  -> Generate dataset.yaml
  -> Run Dataset QA

1. Project root and folder scaffold

Selecting a project root helps IntegraPose keep paths consistent.

Typical structure:

project_root/
  images_all/
  labels_all/
  images/train/
  images/val/
  labels/train/
  labels/val/
  models/
  videos/

2. Define the schema

Field What to enter
Keypoint names The exact keypoint order used by your pose project
Behaviors Optional class IDs and names when your workflow uses behavior classes
Skeleton connections Optional edges for overlays and annotation visuals

3. Choose a labeling route

Built-in annotator

Use the built-in annotator when you want fully manual labeling from the main app.

Typical setup:

  • Image Directory -> your flat image folder, often images_all/
  • Annotation Output Dir -> your label folder, often labels_all/

Assisted Pose Curation

Use Assisted Pose Curation when you want review-first pose labeling with model-assisted suggestions.

Typical flow:

  1. Enable the plugin if needed
  2. Open Open Assisted Pose Curation...
  3. Pull candidate frames
  4. Review and correct suggested poses
  5. Export back into the standard training workflow

4. Create the train/val split

Use Create Train/Val Split Folders after labeling.

This populates:

  • images/train
  • images/val
  • labels/train
  • labels/val

5. Generate dataset.yaml

Use Generate dataset.yaml after the split exists, or point Setup to an existing Ultralytics-style dataset layout.

This file is what the Training tab uses later.

6. Run Dataset QA

Use Dataset QA before training to catch:

  • missing files
  • malformed labels
  • mismatched image/label pairs
  • dataset structure problems

Practical tips

  • Keep keypoint order stable once training starts.
  • Save the project after major setup changes.
  • If you are using a detection-only model workflow, Setup may still be useful for project organization, but the built-in Training tab remains pose-oriented.