voyager-add-model

Add Custom Model

Add a custom model to the Voyager SDK model zoo for Axelera AI hardware

Instructions

Add the specified model: $ARGUMENTS

Step 0: Data Source & Environment Selection

{{INCLUDE common/voyager-sdk-setup.md}}

Step 0.5: Axelera Voyager Project & Task Integration

{{INCLUDE common/voyager-task-integration.md}}

Step 1: Model Source Identification

Determine model source format:

• PyTorch: .pt, .pth files or torchvision/timm model name
• ONNX: .onnx file
• TensorFlow: Convert to ONNX first
• Custom: Define custom model class

Step 2: Model Requirements Analysis

Gather model information:

• Input shape (batch, channels, height, width)
• Input format (RGB/BGR, NCHW/NHWC)
• Normalization parameters (mean, std)
• Output format and shape
• Model task (detection, classification, etc.)

Step 3: SDK-Native YAML Integration

Do not author a freehand YAML schema. Start from the nearest existing SDK YAML under ax_models/zoo/, ax_models/reference/, pipeline-template/, or docs/tutorials/custom-weights.md, then change only fields backed by the model metadata and the Grounding Ledger.

Required SDK YAML fields normally include:

• axelera-model-format, name, description
• pipeline tasks with SDK operators such as letterbox, torch-totensor, and decodeyolo only when verified in the source YAML or docs/reference/pipeline/yaml-operators.md
• models entries with SDK classes such as AxONNXModel, AxTorchvisionResNet, or a types.Model/TorchModel subclass
• class_path, weight_path, optional weight_url and weight_md5
• task_category, input_tensor_layout, input_tensor_shape, input_color_format, dataset
• datasets entries using SDK data adapters such as ObjDataAdapter, TorchvisionDataAdapter, KptDataAdapter, or SegDataAdapter

For ONNX models, prefer class: AxONNXModel and class_path: $AXELERA_FRAMEWORK/ax_models/base_onnx.py unless a closer model-zoo YAML proves a different class is required. For Ultralytics YOLO, copy an existing YOLO model-zoo YAML and update weight_path, num_classes, dataset, and decoder parameters only when the source model metadata proves those values. For torchvision/timm-style classifiers, copy a torchvision classifier YAML and preserve its pipeline-template/torch-imagenet.yaml pattern unless SDK evidence supports another template.