---

name: custom-training-and-data description: "Prepare ImageAI 3.x custom training datasets, conversion helpers, trainer calls, and artifact handoff guidance." disable-model-invocation: true metadata: disco-role: operating license: MIT

Custom Training and Data

Use this operating sub-skill when the task is to prepare data or start custom ImageAI 3.x training for either:

• custom image classification with ClassificationModelTrainer; or
• custom YOLOv3 / TinyYOLOv3 object detection with DetectionModelTrainer.

Do not use this sub-skill to run inference with the produced artifacts. After training, route classification inference to the classification-workflows sub-skill, custom image detection to object-detection-workflows, and custom video detection to video-detection-workflows.

Route by user intent

User intent	Use this file first	Then use
Check whether a classification dataset is trainable	references/data-formats.md and scripts/validate_imageai_dataset.py --task classification	references/troubleshooting.md for failures
Check whether a YOLO detection dataset is trainable	references/data-formats.md and scripts/validate_imageai_dataset.py --task detection	references/training-workflows.md for trainer setup
Convert Pascal VOC XML detection data to ImageAI 3.x YOLO txt layout	scripts/pascal_voc_to_yolo.py	Validate the generated output before training
Train custom classification	references/training-workflows.md	Route generated .pt + classes JSON to classification-workflows
Train custom detection	references/training-workflows.md	Route generated .pt + detection config JSON to object-detection-workflows or video-detection-workflows
Resolve stale ImageAI 2.x / TensorFlow-era parameters	references/troubleshooting.md	Use current PyTorch 3.x signatures only

Current ImageAI 3.x constraints

• ImageAI 3.x uses the PyTorch backend for the active package path.
• .h5 TensorFlow-era pretrained or custom models are not accepted by the current trainer/inference extension checks; use .pt or .pth weights.
• Stale parameters such as enhance_data, num_objects for classification training, loadModel(num_objects=...), speed modes, and standalone detection evaluateModel() belong to older TensorFlow-era docs and must not be passed to the current PyTorch APIs.
• pycocotools is not part of the current custom YOLO training path covered by this sub-skill.

Bundled helpers

Run helpers from this sub-skill directory or pass absolute dataset paths from outside. The helper scripts do not import ImageAI and do not start model training.

python scripts/validate_imageai_dataset.py --task classification --dataset-dir path/to/dataset --strict
python scripts/validate_imageai_dataset.py --task detection --dataset-dir path/to/dataset --strict
python scripts/pascal_voc_to_yolo.py --dataset-dir path/to/voc-dataset --output-dir path/to/yolo-dataset

The converter writes classes.txt; use that class order as object_names_array for DetectionModelTrainer.setTrainConfig(...).

Verification boundary

Safe checks for this sub-skill are dataset validation, Pascal VOC conversion on tiny fixtures, import/signature checks, and artifact-name reasoning. Full custom training is compute- and asset-dependent; do not claim it was cheaply verified unless a real dataset, release/pretrained weights if needed, and a bounded GPU or CPU training run were actually supplied and executed.