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Overview

MinerU VL layout detection module.

MinerU VL can be used for standalone layout detection, returning detected regions with types and bounding boxes.

For full document extraction (layout + content), use MinerUVLTextExtractor from the text_extraction module instead.

Example 
from omnidocs.tasks.layout_extraction import MinerUVLLayoutDetector
from omnidocs.tasks.layout_extraction.mineruvl import MinerUVLLayoutPyTorchConfig

detector = MinerUVLLayoutDetector(
    backend=MinerUVLLayoutPyTorchConfig(device="cuda")
)
result = detector.extract(image)

for box in result.bboxes:
    print(f"{box.label}: {box.confidence:.2f}")

MinerUVLLayoutAPIConfig

Bases: BaseModel

API backend config for MinerU VL layout detection.

Example 
from omnidocs.tasks.layout_extraction import MinerUVLLayoutDetector
from omnidocs.tasks.layout_extraction.mineruvl import MinerUVLLayoutAPIConfig

detector = MinerUVLLayoutDetector(
    backend=MinerUVLLayoutAPIConfig(
        server_url="https://your-server.modal.run"
    )
)
result = detector.extract(image)

MinerUVLLayoutDetector 

MinerUVLLayoutDetector(
    backend: MinerUVLLayoutBackendConfig,
)

Bases: BaseLayoutExtractor

MinerU VL layout detector.

Uses MinerU2.5-2509-1.2B for document layout detection. Detects 22+ element types including text, titles, tables, equations, figures, code, and more.

For full document extraction (layout + content), use MinerUVLTextExtractor from the text_extraction module instead.

Example 
from omnidocs.tasks.layout_extraction import MinerUVLLayoutDetector
from omnidocs.tasks.layout_extraction.mineruvl import MinerUVLLayoutPyTorchConfig

detector = MinerUVLLayoutDetector(
    backend=MinerUVLLayoutPyTorchConfig(device="cuda")
)
result = detector.extract(image)

for box in result.bboxes:
    print(f"{box.label}: {box.confidence:.2f}")

Initialize MinerU VL layout detector.

PARAMETER DESCRIPTION
backend

Backend configuration (PyTorch, VLLM, MLX, or API)

TYPE: MinerUVLLayoutBackendConfig

Source code in omnidocs/tasks/layout_extraction/mineruvl/detector.py 
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def __init__(self, backend: MinerUVLLayoutBackendConfig):
    """
    Initialize MinerU VL layout detector.

    Args:
        backend: Backend configuration (PyTorch, VLLM, MLX, or API)
    """
    self.backend_config = backend
    self._client = None
    self._loaded = False
    self._load_model()

extract 

extract(
    image: Union[Image, ndarray, str, Path],
) -> LayoutOutput

Detect layout elements in the image.

PARAMETER DESCRIPTION
image

Input image (PIL Image, numpy array, or file path)

TYPE: Union[Image, ndarray, str, Path]

RETURNS DESCRIPTION
LayoutOutput

LayoutOutput with standardized labels and bounding boxes
Source code in omnidocs/tasks/layout_extraction/mineruvl/detector.py 
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def extract(
    self,
    image: Union[Image.Image, np.ndarray, str, Path],
) -> LayoutOutput:
    """
    Detect layout elements in the image.

    Args:
        image: Input image (PIL Image, numpy array, or file path)

    Returns:
        LayoutOutput with standardized labels and bounding boxes
    """
    if not self._loaded:
        raise RuntimeError("Model not loaded.")

    pil_image = self._prepare_image(image)
    width, height = pil_image.size

    # Run layout detection
    blocks = self._detect_layout(pil_image)

    # Convert to LayoutOutput
    bboxes = []
    for block in blocks:
        # Convert normalized [0,1] to pixel coords
        x1, y1, x2, y2 = block.bbox
        pixel_bbox = BoundingBox(
            x1=x1 * width,
            y1=y1 * height,
            x2=x2 * width,
            y2=y2 * height,
        )

        # Map label
        label = MINERUVL_LABEL_MAPPING.get(block.type, LayoutLabel.UNKNOWN)

        bboxes.append(
            LayoutBox(
                label=label,
                bbox=pixel_bbox,
                confidence=1.0,  # MinerU VL doesn't output confidence
                original_label=block.type.value,
            )
        )

    return LayoutOutput(
        bboxes=bboxes,
        image_width=width,
        image_height=height,
        model_name="MinerU2.5-2509-1.2B",
    )

MinerUVLLayoutMLXConfig

Bases: BaseModel

MLX backend config for MinerU VL layout detection on Apple Silicon.

Example 
from omnidocs.tasks.layout_extraction import MinerUVLLayoutDetector
from omnidocs.tasks.layout_extraction.mineruvl import MinerUVLLayoutMLXConfig

detector = MinerUVLLayoutDetector(
    backend=MinerUVLLayoutMLXConfig()
)
result = detector.extract(image)

MinerUVLLayoutPyTorchConfig

Bases: BaseModel

PyTorch/HuggingFace backend config for MinerU VL layout detection.

Example 
from omnidocs.tasks.layout_extraction import MinerUVLLayoutDetector
from omnidocs.tasks.layout_extraction.mineruvl import MinerUVLLayoutPyTorchConfig

detector = MinerUVLLayoutDetector(
    backend=MinerUVLLayoutPyTorchConfig(device="cuda")
)
result = detector.extract(image)

MinerUVLLayoutVLLMConfig

Bases: BaseModel

VLLM backend config for MinerU VL layout detection.

Example 
from omnidocs.tasks.layout_extraction import MinerUVLLayoutDetector
from omnidocs.tasks.layout_extraction.mineruvl import MinerUVLLayoutVLLMConfig

detector = MinerUVLLayoutDetector(
    backend=MinerUVLLayoutVLLMConfig(tensor_parallel_size=1)
)
result = detector.extract(image)

api

API backend configuration for MinerU VL layout detection.

MinerUVLLayoutAPIConfig

Bases: BaseModel

API backend config for MinerU VL layout detection.

Example 
from omnidocs.tasks.layout_extraction import MinerUVLLayoutDetector
from omnidocs.tasks.layout_extraction.mineruvl import MinerUVLLayoutAPIConfig

detector = MinerUVLLayoutDetector(
    backend=MinerUVLLayoutAPIConfig(
        server_url="https://your-server.modal.run"
    )
)
result = detector.extract(image)

detector

MinerU VL layout detector.

Uses MinerU2.5-2509-1.2B for document layout detection. Detects 22+ element types including text, titles, tables, equations, figures, code.

MinerUVLLayoutDetector 

MinerUVLLayoutDetector(
    backend: MinerUVLLayoutBackendConfig,
)

Bases: BaseLayoutExtractor

MinerU VL layout detector.

Uses MinerU2.5-2509-1.2B for document layout detection. Detects 22+ element types including text, titles, tables, equations, figures, code, and more.

For full document extraction (layout + content), use MinerUVLTextExtractor from the text_extraction module instead.

Example 
from omnidocs.tasks.layout_extraction import MinerUVLLayoutDetector
from omnidocs.tasks.layout_extraction.mineruvl import MinerUVLLayoutPyTorchConfig

detector = MinerUVLLayoutDetector(
    backend=MinerUVLLayoutPyTorchConfig(device="cuda")
)
result = detector.extract(image)

for box in result.bboxes:
    print(f"{box.label}: {box.confidence:.2f}")

Initialize MinerU VL layout detector.

PARAMETER DESCRIPTION
backend

Backend configuration (PyTorch, VLLM, MLX, or API)

TYPE: MinerUVLLayoutBackendConfig

Source code in omnidocs/tasks/layout_extraction/mineruvl/detector.py 
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def __init__(self, backend: MinerUVLLayoutBackendConfig):
    """
    Initialize MinerU VL layout detector.

    Args:
        backend: Backend configuration (PyTorch, VLLM, MLX, or API)
    """
    self.backend_config = backend
    self._client = None
    self._loaded = False
    self._load_model()

extract 

extract(
    image: Union[Image, ndarray, str, Path],
) -> LayoutOutput

Detect layout elements in the image.

PARAMETER DESCRIPTION
image

Input image (PIL Image, numpy array, or file path)

TYPE: Union[Image, ndarray, str, Path]

RETURNS DESCRIPTION
LayoutOutput

LayoutOutput with standardized labels and bounding boxes
Source code in omnidocs/tasks/layout_extraction/mineruvl/detector.py 
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def extract(
    self,
    image: Union[Image.Image, np.ndarray, str, Path],
) -> LayoutOutput:
    """
    Detect layout elements in the image.

    Args:
        image: Input image (PIL Image, numpy array, or file path)

    Returns:
        LayoutOutput with standardized labels and bounding boxes
    """
    if not self._loaded:
        raise RuntimeError("Model not loaded.")

    pil_image = self._prepare_image(image)
    width, height = pil_image.size

    # Run layout detection
    blocks = self._detect_layout(pil_image)

    # Convert to LayoutOutput
    bboxes = []
    for block in blocks:
        # Convert normalized [0,1] to pixel coords
        x1, y1, x2, y2 = block.bbox
        pixel_bbox = BoundingBox(
            x1=x1 * width,
            y1=y1 * height,
            x2=x2 * width,
            y2=y2 * height,
        )

        # Map label
        label = MINERUVL_LABEL_MAPPING.get(block.type, LayoutLabel.UNKNOWN)

        bboxes.append(
            LayoutBox(
                label=label,
                bbox=pixel_bbox,
                confidence=1.0,  # MinerU VL doesn't output confidence
                original_label=block.type.value,
            )
        )

    return LayoutOutput(
        bboxes=bboxes,
        image_width=width,
        image_height=height,
        model_name="MinerU2.5-2509-1.2B",
    )

mlx

MLX backend configuration for MinerU VL layout detection (Apple Silicon).

MinerUVLLayoutMLXConfig

Bases: BaseModel

MLX backend config for MinerU VL layout detection on Apple Silicon.

Example 
from omnidocs.tasks.layout_extraction import MinerUVLLayoutDetector
from omnidocs.tasks.layout_extraction.mineruvl import MinerUVLLayoutMLXConfig

detector = MinerUVLLayoutDetector(
    backend=MinerUVLLayoutMLXConfig()
)
result = detector.extract(image)

pytorch

PyTorch backend configuration for MinerU VL layout detection.

MinerUVLLayoutPyTorchConfig

Bases: BaseModel

PyTorch/HuggingFace backend config for MinerU VL layout detection.

Example 
from omnidocs.tasks.layout_extraction import MinerUVLLayoutDetector
from omnidocs.tasks.layout_extraction.mineruvl import MinerUVLLayoutPyTorchConfig

detector = MinerUVLLayoutDetector(
    backend=MinerUVLLayoutPyTorchConfig(device="cuda")
)
result = detector.extract(image)

vllm

VLLM backend configuration for MinerU VL layout detection.

MinerUVLLayoutVLLMConfig

Bases: BaseModel

VLLM backend config for MinerU VL layout detection.

Example 
from omnidocs.tasks.layout_extraction import MinerUVLLayoutDetector
from omnidocs.tasks.layout_extraction.mineruvl import MinerUVLLayoutVLLMConfig

detector = MinerUVLLayoutDetector(
    backend=MinerUVLLayoutVLLMConfig(tensor_parallel_size=1)
)
result = detector.extract(image)