altametris.sara.yolo.model

YOLO model implementation.

Wrapper around Ultralytics YOLO with standardized Altametris interface. Supports YOLOv8 and YOLO11 for detection, segmentation, OBB, and pose tasks.

Example

>>> model = YoloModel(version="11", task="detect", size="x", pretrained=True)
>>> x = torch.randn(1, 3, 640, 640)
>>> output = model(x)

Attributes

logger

Classes

YoloModel

YOLO model wrapper implementing BaseModel interface.

Module Contents

altametris.sara.yolo.model.logger
class altametris.sara.yolo.model.YoloModel(model_path: str | pathlib.Path | None = None, version: str = '11', task: str = 'detect', size: str = 'x', pretrained: bool = False, device: str = 'auto', **kwargs: Any)

Bases: altametris.sara.core.base_model.BaseModel

YOLO model wrapper implementing BaseModel interface.

Provides standardized interface for YOLO models from Ultralytics with support for multiple versions, tasks, and sizes.

Parameters:

  • model_path – Path to existing model weights (optional)

  • version – YOLO version (“11” or “v8”)

  • task – YOLO task (“detect”, “segment”, “obb”, “pose”, “classify”)

  • size – Model size (“n”, “s”, “m”, “l”, “x”)

  • pretrained – Load pretrained weights from Ultralytics

  • device – Device for model (“cpu”, “cuda”, “mps”, “auto”)

Example

>>> # Create from scratch
>>> model = YoloModel(version="11", task="detect", size="x")
>>>
>>> # Load from weights
>>> model = YoloModel(model_path="weights/best.pt")
SUPPORTED_VERSIONS = ['11', 'v8']
SUPPORTED_TASKS = ['detect', 'segment', 'obb', 'pose', 'classify']
SUPPORTED_SIZES = ['n', 's', 'm', 'l', 'x']
yolo: ultralytics.YOLO
_validate_init_params(version: str, task: str, size: str, model_path: str | pathlib.Path | None, pretrained: bool) → None

Validate initialization parameters.

_get_model_name(version: str, task: str, size: str) → str

Generate Ultralytics model name.

Parameters:

  • version – YOLO version

  • task – Task type

  • size – Model size

Returns:

Model name string (e.g., “yolo11x-seg.pt”)

forward(x: torch.Tensor, **kwargs: Any) → Any

Forward pass through YOLO model.

Parameters:

  • x – Input tensor (B, C, H, W)

  • **kwargs – Additional forward arguments

Returns:

Model output

Example

>>> x = torch.randn(1, 3, 640, 640)
>>> output = model(x)
load_weights(path: str | pathlib.Path, **kwargs: Any) → None

Load weights from file.

Parameters:

  • path – Path to weights file

  • **kwargs – Additional loading arguments

Raises:

ModelError – If weights cannot be loaded

save_weights(path: str | pathlib.Path, **kwargs: Any) → None

Save model weights in Ultralytics checkpoint format.

This method uses the native Ultralytics save() API which creates a complete checkpoint file compatible with YOLO loading.

Parameters:

  • path – Path to save weights

  • **kwargs – Additional saving arguments

Example

>>> model.save_weights("weights/my_model.pt")

Note

The saved file is a complete Ultralytics checkpoint that can be reloaded with YoloModel(model_path=path) or YOLO(path).

property names: dict[int, str]

Get class names mapping.

property task: str

Get model task.

property version: str

Get YOLO version.

__repr__() → str

String representation.