Skip to main content
Version: v1.8

Custom Model Architecture

Experimental

This feature is in beta. Some parts of the workflow require implementing SDK-internal Python classes. API stability is not guaranteed between releases.

How to deploy a completely new model architecture — one that isn't in the Model Zoo and requires a custom decoder, dataset adapter, or evaluator.

Start here first: If your model uses a standard architecture (YOLO, ResNet, etc.) with custom weights, use Deploy Custom Weights instead — it's much simpler and doesn't require implementing any SDK classes.

When you need this​

You need the custom model workflow when:

  • Your model architecture is not in the Model Zoo
  • Your output tensor format requires a custom decoder (to convert raw tensors to bounding boxes, class labels, etc.)
  • Your dataset uses a labeling format not supported by the built-in adapters
  • You need a custom accuracy metric

The Voyager framework APIs​

The SDK provides five extension points. You only implement the ones you need:

APIWhat you implementExample in SDK
types.ModelA deployable PyTorch or ONNX model class. Referenced in YAML models section.AxUltralyticsYOLO
types.DataAdapterA dataset adapter: outputs images and ground truth in AxTaskMeta format. Referenced in YAML datasets section.ObjDataAdapter
types.EvaluatorCompares inference results against ground truth to compute accuracy metrics (e.g. mAP). Defined as a property of a DataAdapter.ObjectEvaluator
AxOperatorA host-side pipeline element: pre-processing or post-processing (resize, normalize, etc.).Resize
AxOperator decoderAn AxOperator that converts raw model output tensors to AxTaskMeta metadata. Referenced in YAML operators section.YoloDecode

All components communicate through AxTaskMeta — the SDK's common metadata representation for a task category (object detection, classification, etc.).

Workflow overview​

  1. Define your model class — subclass types.Model, implement the required methods, point weight_path at your weights file.
  2. Define a decoder — subclass AxOperator, implement the tensor-to-metadata conversion for your model's output format.
  3. Define a data adapter (if needed) — subclass types.DataAdapter to load your dataset.
  4. Wire it up in YAML — reference your classes in the models, datasets, and operators sections.
  5. Deploy — run ./deploy.py as with any other model.
Image operators are stripped during compilation

The Axelera model compiler removes any image pre-processing operators from the source model definition before compiling for Metis hardware. You must specify those operations as pipeline operators in your YAML file. If you don't, the end-to-end pipeline will run without the preprocessing your model was trained with, silently producing incorrect results.

Getting started​

The best starting point is the source of an existing Model Zoo model that's architecturally similar to yours. Browse:

  • ax_models/yolo/ax_yolo.py — YOLO model class
  • ax_datasets/objdataadapter.py — object detection data adapter
  • ax_evaluators/obj_eval.py — mAP evaluator
  • ax_models/decoders/yolo.py — YOLO decoder operator

See also​