Mobilint Vision Python

Run pre-trained Mobilint Vision models from Python. mblt-vision-python provides model configuration, artifact loading, preprocessing, inference integration, and typed postprocessing results for image classification, depth estimation, face and object detection, OBB, instance and semantic segmentation, and pose estimation.

Version 0.0.0 is the initial standalone release.

Installation

pip install mblt-vision-python

MXQ inference requires a supported Mobilint NPU environment. Model artifacts are downloaded from the Mobilint Hugging Face organization when no local model_path is supplied. For ONNX execution, install one of the optional extras:

pip install "mblt-vision-python[onnxruntime]"
# Or, on supported systems:
pip install "mblt-vision-python[onnxruntime-gpu]"

Quick start

Most models include matching preprocess/postprocess behavior around a raw call (mask generation is the exception -- see below):

from mblt_vision import ResNet50

model = ResNet50()
x = model.preprocess("image.jpg")
result = model.postprocess(model(x))

For configurable model selection and local MXQ or ONNX artifacts, use MBLT_Engine:

from mblt_vision import MBLT_Engine

model = MBLT_Engine(model_cls="resnet50", model_type="DEFAULT")
try:
    result = model.postprocess(model(model.preprocess("image.jpg")))
finally:
    model.dispose()

Discover supported tasks and models with list_tasks() and list_models(). New code should use the task subpackages (for example, mblt_vision.object_detection) or MBLT_Engine. Top-level model imports such as from mblt_vision import ResNet50 remain supported for convenience.

obb is the canonical oriented-bounding-box task name.

Mask generation is promptable, so SAM2HieraLarge takes point prompts and returns candidate masks from a single predict() call instead of the separate preprocess/raw-call/postprocess steps above:

from mblt_vision.mask_generation import SAM2HieraLarge

model = SAM2HieraLarge()  # framework="onnx" for ONNX Runtime inference
result = model.predict("image.jpg", points=[[320, 240]], labels=[1])

See the mask generation section for the prompt contract, the two-artifact layout, and SA-V validation.

Model Zoo migration

Vision is now maintained in this package. mblt-model-zoo retains mblt_model_zoo.vision as a compatibility facade for existing applications; new projects should import from mblt_vision directly. Its mblt-model-zoo predict, val, and compile commands also delegate to this package.

Command line

The standalone package provides the mblt-vision command with predict, val, and compile subcommands:

mblt-vision predict --source image.jpg --model resnet50

predict is the single inference command for classification, depth estimation, object and face detection, instance and semantic segmentation, OBB, pose estimation, and point-prompted mask generation. The selected model determines its task and processing pipeline. By default it downloads the model artifact and saves a plotted result under runs/vision/predict/. Use --output to choose the result-image path, --topk for classification labels, and --conf-thres/--iou-thres for detection-style tasks. --framework onnx selects ONNX Runtime inference; --target-device and --core-mode select the MXQ board/runtime mode.

Mask generation models require 1-3 point prompts (--point X,Y,LABEL, where LABEL is 1 for positive and 0 for negative) and load two artifacts, overridden with --encoder-mxq-path/--decoder-mxq-path (or the --encoder-onnx-path/ --decoder-onnx-path pair) rather than --model-path/--mxq-path/--onnx-path.

mblt-vision predict --source image.jpg --model yolo11m --conf-thres 0.4 --output result.jpg
mblt-vision predict --source image.jpg --model yolo11m-pose --target-device regulus-ra --core-mode single
mblt-vision predict --source image.jpg --model sam2-hiera-large --point 320,240,1

The corresponding mblt-model-zoo commands use the same standalone handlers for backward compatibility.

Documentation and tests

See the Vision API guide for supported model families, model details, artifact selection, and output taxonomy behavior. See the compilation guide for calibration-data preparation and MXQ compilation. The test guide explains offline, Hugging Face, and NPU test runs.

Support and issues

For installation, model, or runtime support, visit the Mobilint forum. Report reproducible package issues in the mblt-vision-python issue tracker.

License

Distributed under the BSD 3-Clause License.