# NN Archive Superblob The example showcases loading a YOLOv6-Nano superblob model from the model zoo, configuring a neural network node with a 416x416 RGB camera input, and processing detection and passthrough outputs. This example requires the DepthAI v3 API, see [installation instructions](https://docs.luxonis.com/software-v3/depthai.md). ## Pipeline ### examples/nn_archive_superblob.pipeline.json ```json {"pipeline": {"connections": [{"node1Id": 1, "node1Output": "passthrough", "node1OutputGroup": "", "node2Id": 4, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 1, "node1Output": "out", "node1OutputGroup": "", "node2Id": 2, "node2Input": "in", "node2InputGroup": ""}, {"node1Id": 0, "node1Output": "0", "node1OutputGroup": "dynamicOutputs", "node2Id": 1, "node2Input": "in", "node2InputGroup": ""}], "globalProperties": {"calibData": null, "cameraTuningBlobSize": null, "cameraTuningBlobUri": "", "leonCssFrequencyHz": 700000000.0, "leonMssFrequencyHz": 700000000.0, "pipelineName": null, "pipelineVersion": null, "sippBufferSize": 18432, "sippDmaBufferSize": 16384, "xlinkChunkSize": -1}, "nodes": [[4, {"alias": "", "id": 4, "ioInfo": [[["", "in"], {"blocking": true, "group": "", "id": 8, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkOut", "parentId": -1, "properties": {"maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "__x_1_passthrough"}}], [2, {"alias": "", "id": 2, "ioInfo": [[["", "in"], {"blocking": true, "group": "", "id": 7, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "XLinkOut", "parentId": -1, "properties": {"maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "__x_1_out"}}], [1, {"alias": "", "id": 1, "ioInfo": [[["", "passthrough"], {"blocking": false, "group": "", "id": 6, "name": "passthrough", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "out"], {"blocking": false, "group": "", "id": 5, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "in"], {"blocking": true, "group": "", "id": 4, "name": "in", "queueSize": 3, "type": 3, "waitForMessage": true}]], "logLevel": 3, "name": "NeuralNetwork", "parentId": -1, "properties": {"backend": "", "backendProperties": {}, "blobSize": 8691690, "blobUri": "asset:__blob", "modelSource": 0, "modelUri": "", "numFrames": 8, "numNCEPerThread": 0, "numShavesPerThread": 0, "numThreads": 2}}], [0, {"alias": "", "id": 0, "ioInfo": [[["dynamicOutputs", "0"], {"blocking": false, "group": "dynamicOutputs", "id": 3, "name": "0", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "raw"], {"blocking": false, "group": "", "id": 2, "name": "raw", "queueSize": 8, "type": 0, "waitForMessage": false}], [["", "mockIsp"], {"blocking": true, "group": "", "id": 1, "name": "mockIsp", "queueSize": 8, "type": 3, "waitForMessage": false}], [["", "inputControl"], {"blocking": true, "group": "", "id": 0, "name": "inputControl", "queueSize": 3, "type": 3, "waitForMessage": false}]], "logLevel": 3, "name": "Camera", "parentId": -1, "properties": {"boardSocket": 0, "cameraName": "", "fps": -1.0, "imageOrientation": -1, "initialControl": {"aeLockMode": false, "aeMaxExposureTimeUs": 1647981365, "aeRegion": {"height": 14390, "priority": 979723322, "width": 14948, "x": 24929, "y": 25146}, "afRegion": {"height": 26214, "priority": 979788602, "width": 14901, "x": 25953, "y": 13626}, "antiBandingMode": 58, "autoFocusMode": 3, "awbLockMode": false, "awbMode": 55, "brightness": 98, "captureIntent": 49, "chromaDenoise": 53, "cmdMask": 0, "contrast": 58, "controlMode": 52, "effectMode": 58, "enableHdr": false, "expCompensation": 48, "expManual": {"exposureTimeUs": 909796454, "frameDurationUs": 979644730, "sensitivityIso": 1681013345}, "frameSyncMode": 49, "lensPosAutoInfinity": 58, "lensPosAutoMacro": 102, "lensPosition": 0, "lensPositionRaw": 0.0, "lowPowerNumFramesBurst": 54, "lowPowerNumFramesDiscard": 58, "lumaDenoise": 58, "miscControls": [], "saturation": 51, "sceneMode": 51, "sharpness": 52, "strobeConfig": {"activeLevel": 58, "enable": 97, "gpioNumber": 98}, "strobeTimings": {"durationUs": 976434746, "exposureBeginOffsetUs": 825909560, "exposureEndOffsetUs": 925973042}, "wbColorTemp": 13626}, "isp3aFps": 0, "mockIspHeight": -1, "mockIspWidth": -1, "numFramesPoolIsp": 3, "numFramesPoolPreview": 4, "numFramesPoolRaw": 3, "numFramesPoolStill": 4, "numFramesPoolVideo": 4, "outputRequests": [{"enableUndistortion": null, "fps": {"value": null}, "resizeMode": 0, "size": {"value": {"index": 0, "value": [416, 416]}}, "type": 8}], "resolutionHeight": -1, "resolutionWidth": -1}}]]}} ``` ## Source code #### Python ```python #!/usr/bin/env python3 import time import depthai as dai # We will download a blob NNArchive from the model zoo # Pick your own model from modelDescription = dai.NNModelDescription() modelDescription.model = "yolov6-nano" modelDescription.platform = "RVC2" # Download model from zoo and load it archivePath = dai.getModelFromZoo(modelDescription, useCached=True) archive = dai.NNArchive(archivePath) # Archive knows it is a blob archive assert archive.getModelType() == dai.ModelType.SUPERBLOB # Therefore, getSuperBlob() is available assert archive.getSuperBlob() is not None # There is no blob or other model format available assert archive.getBlob() is None assert archive.getOtherModelFormat() is None # You can access any config version v1config: dai.nn_archive.v1.Config = archive.getConfig() # Print some config fields print("-" * 10) print("Config fields:") print(f"\tConfig version: {v1config.configVersion}") print(f"\tModel heads: {v1config.model.heads}") print(f"\tModel inputs: {v1config.model.inputs}") print(f"\tModel metadata: {v1config.model.metadata}") print(f"\tModel outputs: {v1config.model.outputs}") print("-" * 10) with dai.Pipeline() as pipeline: # Color camera node camRgb = pipeline.create(dai.node.Camera).build() outCam = camRgb.requestOutput((416, 416), dai.ImgFrame.Type.BGR888p) # Neural network node blob = archive.getSuperBlob().getBlobWithNumShaves(6) neuralNetwork = pipeline.create(dai.node.NeuralNetwork) neuralNetwork.setBlob(blob) neuralNetwork.setNumInferenceThreads(2) # Linking outCam.link(neuralNetwork.input) nnDetectionQueue = neuralNetwork.out.createOutputQueue() nnPassthroughQueue = neuralNetwork.passthrough.createOutputQueue() pipeline.start() while pipeline.isRunning(): in_nn = nnDetectionQueue.get() in_nnPassthrough = nnPassthroughQueue.get() print("Data received") time.sleep(0.1) ``` #### C++ ```cpp #include #include #include #include #include #include #include "depthai/depthai.hpp" #include "depthai/openvino/OpenVINO.hpp" // Global flag for graceful shutdown std::atomic quitEvent(false); // Signal handler void signalHandler(int signum) { quitEvent = true; } int main() { // Set up signal handlers signal(SIGTERM, signalHandler); signal(SIGINT, signalHandler); try { // Get model from zoo dai::NNModelDescription modelDesc; modelDesc.model = "yolov6-nano"; modelDesc.platform = "RVC2"; auto archivePath = dai::getModelFromZoo(modelDesc, true); // true to use cached if available, otherwise re-download // Load NN archive dai::NNArchive archive(archivePath); // Verify archive type and properties if(archive.getModelType() != dai::model::ModelType::SUPERBLOB) { throw std::runtime_error("Archive is not a superblob type"); } if(!archive.getSuperBlob()) { throw std::runtime_error("SuperBlob should not be null for superblob type"); } if(archive.getBlob()) { throw std::runtime_error("Blob should be null for superblob type"); } // Get config and print some fields auto config = archive.getConfig(); std::cout << "----------" << std::endl; std::cout << "Config fields:" << std::endl; std::cout << "\tConfig version: " << config.configVersion.value() << std::endl; std::cout << "\tModel heads: " << config.model.heads.value().size() << std::endl; std::cout << "\tModel inputs: " << config.model.inputs.size() << std::endl; std::cout << "\tModel outputs: " << config.model.outputs.size() << std::endl; std::cout << "----------" << std::endl; // Create pipeline dai::Pipeline pipeline; // Color camera node auto camRgb = pipeline.create()->build(); auto camOut = camRgb->requestOutput(std::make_pair(416, 416), dai::ImgFrame::Type::BGR888p); // Neural network node auto neuralNetwork = pipeline.create(); neuralNetwork->setBlob(archive.getSuperBlob()->getBlobWithNumShaves(6)); neuralNetwork->setNumInferenceThreads(2); // Linking camOut->link(neuralNetwork->input); // Create output queues auto qDet = neuralNetwork->out.createOutputQueue(); auto qPassthrough = neuralNetwork->passthrough.createOutputQueue(); // Start pipeline pipeline.start(); while(pipeline.isRunning() && !quitEvent) { auto inDet = qDet->get(); auto inPassthrough = qPassthrough->get(); if(inDet != nullptr) { std::cout << "Detection data received" << std::endl; } if(inPassthrough != nullptr) { std::cout << "Passthrough frame received" << std::endl; } std::this_thread::sleep_for(std::chrono::milliseconds(100)); } // Cleanup pipeline.stop(); pipeline.wait(); } catch(const std::exception& e) { std::cerr << "Error: " << e.what() << std::endl; return 1; } return 0; } ``` ### Need assistance? 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