MobileNetDetectionNetwork ========================= MobileNet detection network node is very similar to :ref:`NeuralNetwork` (in fact it extends it). The only difference is that this node is specifically for the MobileNet NN and it decodes the result of the NN on device. This means that :code:`out` of this node is not a byte array but a :ref:`ImgDetections` that can easily be used in your code. How to place it ############### .. tabs:: .. code-tab:: py pipeline = dai.Pipeline() mobilenetDet = pipeline.create(dai.node.MobileNetDetectionNetwork) .. code-tab:: c++ dai::Pipeline pipeline; auto mobilenetDet = pipeline.create(); Inputs and Outputs ################## .. code-block:: ┌───────────────────┐ │ │ out │ ├───────────► │ MobileNet │ │ Detection │ input │ Network │ passthrough ───────────►│-------------------├───────────► │ │ └───────────────────┘ **Message types** - :code:`input` - :ref:`ImgFrame` - :code:`out` - :ref:`ImgDetections` - :code:`passthrough` - :ref:`ImgFrame` Usage ##### .. tabs:: .. code-tab:: py pipeline = dai.Pipeline() mobilenetDet = pipeline.create(dai.node.MobileNetDetectionNetwork) mobilenetDet.setConfidenceThreshold(0.5) mobilenetDet.setBlobPath(nnBlobPath) mobilenetDet.setNumInferenceThreads(2) mobilenetDet.input.setBlocking(False) .. code-tab:: c++ dai::Pipeline pipeline; auto mobilenetDet = pipeline.create(); mobilenetDet->setConfidenceThreshold(0.5f); mobilenetDet->setBlobPath(nnBlobPath); mobilenetDet->setNumInferenceThreads(2); mobilenetDet->input.setBlocking(false); Examples of functionality ######################### - :ref:`RGB & MobilenetSSD` - :ref:`Mono & MobilenetSSD` - :ref:`Mono & MobilenetSSD & Depth` Reference ######### .. tabs:: .. tab:: Python .. autoclass:: depthai.node.MobileNetDetectionNetwork :members: :inherited-members: :noindex: .. tab:: C++ .. doxygenclass:: dai::node::MobileNetDetectionNetwork :project: depthai-core :members: :private-members: :undoc-members: .. include:: ../../includes/footer-short.rst