dataeval.models.LiteRtObjectDetector¶

class dataeval.models.LiteRtObjectDetector(model_path, metadata_path, *, image_size=None, boxes_name='boxes', scores_name='scores')¶

Opinionated object detector backed by LiteRT (TensorFlow Lite).

Loads an exported .tflite object-detection model via a LiteRT interpreter together with its model-metadata.json contract, then maps a batch of CHW images to per-image detection targets. Calling the instance preprocesses each image to the model’s input contract (color layout, size, [0, 1] normalization), runs inference, and returns one detection target per image. Requires dataeval[tflite].

Parameters:¶

model_path : str or Path¶

Path to the exported .tflite model file.

metadata_path : str or Path¶

Path to the model-metadata.json describing the input/output contract. Its declared task must be IMAGE_OBJECT_DETECTION.

image_size : tuple[int, int] or None, default None¶

Optional (height, width) override for the model’s input size. When set, images are resized to this size instead of the size declared in metadata; required when the metadata declares a variable (-1) dimension.

boxes_name : str, default "boxes"¶

Name of the model output tensor holding detection boxes.

scores_name : str, default "scores"¶

Name of the model output tensor holding per-class detection scores.

Raises:¶

• ValueError – If the metadata declares a task other than IMAGE_OBJECT_DETECTION.

• ImportError – If neither tflite-runtime nor tensorflow is installed.

• FileNotFoundError – If model_path does not exist.

See also

OnnxObjectDetector

Same detector backed by ONNX Runtime.

LiteRtImageClassifier

Opinionated image classifier backed by LiteRT.

read_model_metadata

Parse the metadata contract.

Notes

Implements the MAITE object_detection.Model protocol: instances expose a ModelMetadata metadata attribute and are callable on a batch of images, returning one MAITE ObjectDetectionTarget per image (with labels taken as the argmax over each detection’s class scores).