[{"data":1,"prerenderedAt":5624},["ShallowReactive",2],{"docs-\u002Fdocs\u002Fflowfuse-nodes\u002Fai\u002Fobject-detection\u002F":3,"featureCatalog":4174,"docs-nav":4579},{"id":4,"title":5,"body":6,"description":4162,"editUrl":4163,"extension":4164,"layout":4165,"meta":4166,"metaTitle":5,"navGroup":4165,"navGroupOrder":4165,"navOrder":4167,"navTitle":5,"navigation":4168,"originalPath":4165,"path":4169,"redirect":4165,"seo":4170,"stem":4171,"updated":4172,"version":4165,"__hash__":4173},"docs\u002Fdocs\u002Fflowfuse-nodes\u002Fai\u002Fobject-detection.md","Object Detection",{"type":7,"value":8,"toc":4142},"minimark",[9,13,33,40,45,50,91,95,123,132,136,147,151,159,162,166,169,324,329,332,338,372,375,380,432,436,440,447,623,626,649,653,656,763,770,774,811,815,4138],[10,11,5],"h1",{"id":12},"object-detection",[14,15,16,17,20,21,24,25,28,29,32],"p",{},"The ",[18,19,5],"strong",{}," node enables detection of objects within images using ",[18,22,23],{},"ONNX models",".\nIt supports a wide range of architectures, including ",[18,26,27],{},"DETR"," and ",[18,30,31],{},"YOLO",", and accepts image data in multiple formats such as Buffers, base64 strings, or tensors.",[14,34,35,36,39],{},"This node is ideal for computer vision use cases like identifying objects in images, counting items, or performing scene analysis directly within ",[18,37,38],{},"Node-RED",".",[41,42,44],"h2",{"id":43},"inputs","Inputs",[46,47,49],"h3",{"id":48},"general","General",[51,52,53,64,80],"ul",{},[54,55,56,59,60],"li",{},[18,57,58],{},"Property:"," ",[61,62,63],"code",{},"input",[54,65,66,59,69,72,73,72,76,79],{},[18,67,68],{},"Type:",[61,70,71],{},"object",", ",[61,74,75],{},"buffer",[61,77,78],{},"string"," or tensor.",[54,81,82,85,86,90],{},[18,83,84],{},"Description:"," The input image or tensor to classify. See the ",[87,88,89],"em",{},"Details"," section for supported formats.",[46,92,94],{"id":93},"model-selection","Model Selection",[51,96,97,103],{},[54,98,99,102],{},[18,100,101],{},"model:"," Path to a local ONNX model file or the name of a model to download from Hugging Face.",[54,104,105,108,109,112,113,72,116,72,119,122],{},[18,106,107],{},"type:"," Data type for the model when using a model name. Supported values include ",[61,110,111],{},"q8"," (default), ",[61,114,115],{},"fp16",[61,117,118],{},"fp32",[61,120,121],{},"int8",", and others.",[124,125,126],"blockquote",{},[14,127,128,131],{},[18,129,130],{},"Note:","\nWhen a model name is provided, the node automatically downloads and caches it locally if it is not already available.",[46,133,135],{"id":134},"configuration","Configuration",[51,137,138],{},[54,139,140,143,144,39],{},[18,141,142],{},"threshold:"," Minimum confidence score (0.0–1.0) required for a prediction to be included in the output.\nThis can also be passed dynamically via ",[61,145,146],{},"msg.threshold",[41,148,150],{"id":149},"outputs","Outputs",[51,152,153],{},[54,154,155,158],{},[18,156,157],{},"payload:"," Contains the detection results.\nDepending on the model type and processing support, the output can be an array or an object.",[41,160,89],{"id":161},"details",[46,163,165],{"id":164},"supported-input-formats","Supported Input Formats",[14,167,168],{},"The node supports multiple input formats depending on the model’s requirements:",[51,170,171,177,183,193],{},[54,172,173,176],{},[18,174,175],{},"Buffer",", Binary image data, typically from a file or camera input.",[54,178,179,182],{},[18,180,181],{},"Base64 string",", Base64-encoded image data.",[54,184,185,188,189,192],{},[18,186,187],{},"Jimp Image Object",", An image object (e.g, output from ",[61,190,191],{},"node-red-contrib-image-tools",").",[54,194,195,198,199],{},[18,196,197],{},"Tensor",", A pre-processed tensor object in the following format:",[200,201,206],"pre",{"className":202,"code":203,"language":204,"meta":205,"style":205},"language-json shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","{\n  \"data\": [0.0, 0.1, 0.2, ...],\n  \"type\": \"float32\",\n  \"dim\": [1, 3, 224, 224]\n}\n","json","",[61,207,208,217,260,284,318],{"__ignoreMap":205},[209,210,213],"span",{"class":211,"line":212},"line",1,[209,214,216],{"class":215},"sMK4o","{\n",[209,218,220,223,227,230,233,236,240,243,246,248,251,253,257],{"class":211,"line":219},2,[209,221,222],{"class":215},"  \"",[209,224,226],{"class":225},"spNyl","data",[209,228,229],{"class":215},"\"",[209,231,232],{"class":215},":",[209,234,235],{"class":215}," [",[209,237,239],{"class":238},"sbssI","0.0",[209,241,242],{"class":215},",",[209,244,245],{"class":238}," 0.1",[209,247,242],{"class":215},[209,249,250],{"class":238}," 0.2",[209,252,242],{"class":215},[209,254,256],{"class":255},"sTEyZ"," ...",[209,258,259],{"class":215},"],\n",[209,261,263,265,268,270,272,275,279,281],{"class":211,"line":262},3,[209,264,222],{"class":215},[209,266,267],{"class":225},"type",[209,269,229],{"class":215},[209,271,232],{"class":215},[209,273,274],{"class":215}," \"",[209,276,278],{"class":277},"sfazB","float32",[209,280,229],{"class":215},[209,282,283],{"class":215},",\n",[209,285,287,289,292,294,296,298,301,303,306,308,311,313,315],{"class":211,"line":286},4,[209,288,222],{"class":215},[209,290,291],{"class":225},"dim",[209,293,229],{"class":215},[209,295,232],{"class":215},[209,297,235],{"class":215},[209,299,300],{"class":238},"1",[209,302,242],{"class":215},[209,304,305],{"class":238}," 3",[209,307,242],{"class":215},[209,309,310],{"class":238}," 224",[209,312,242],{"class":215},[209,314,310],{"class":238},[209,316,317],{"class":215},"]\n",[209,319,321],{"class":211,"line":320},5,[209,322,323],{"class":215},"}\n",[124,325,326],{},[14,327,328],{},"TIP: If the model supports batching, the input can be an array of images in one of the supported formats.",[46,330,94],{"id":331},"model-selection-1",[14,333,16,334,337],{},[61,335,336],{},"model"," property defines which ONNX model to use. You can either:",[51,339,340,351],{},[54,341,342,343,346,347,350],{},"Provide a ",[18,344,345],{},"local path"," (for example, ",[61,348,349],{},"\u002Fdata\u002Fmodels\u002Fyolov5.onnx","), or",[54,352,353,354,357,358,346,367,192],{},"Specify a ",[18,355,356],{},"model name"," available on ",[18,359,360],{},[361,362,366],"a",{"href":363,"rel":364},"https:\u002F\u002Fhuggingface.co\u002Fmodels?pipeline_tag=object-detection&library=transformers.js,onnx&sort=trending",[365],"nofollow","Hugging Face",[361,368,371],{"href":369,"rel":370},"https:\u002F\u002Fhuggingface.co\u002FXenova\u002Fdetr-resnet-50",[365],"Xenova\u002Fdetr-resnet-50",[14,373,374],{},"When a model name is provided, it is automatically fetched and cached locally for reuse.",[376,377,379],"h4",{"id":378},"model-type-options","Model Type Options",[51,381,382,388,393,398,403,409,414,420,426],{},[54,383,384,387],{},[61,385,386],{},"auto",", Automatically selects the most suitable type.",[54,389,390,392],{},[61,391,118],{},", Standard 32-bit floating-point model.",[54,394,395,397],{},[61,396,115],{},", Half-precision 16-bit floating-point model.",[54,399,400,402],{},[61,401,121],{},", 8-bit integer quantized model.",[54,404,405,408],{},[61,406,407],{},"uint8",", 8-bit unsigned integer model.",[54,410,411,413],{},[61,412,111],{},", Quantized Int8 model (default).",[54,415,416,419],{},[61,417,418],{},"q4",", Quantized Int4 model.",[54,421,422,425],{},[61,423,424],{},"q4f16",", Quantized Int4 with Float16 model.",[54,427,428,431],{},[61,429,430],{},"bnb4",", BNB4 quantized model.",[46,433,435],{"id":434},"output-format","Output Format",[376,437,439],{"id":438},"when-supported-yolodetr-models","When Supported (YOLO\u002FDETR Models)",[14,441,442,443,446],{},"If the model output is recognized by the node, ",[61,444,445],{},"msg.payload"," contains structured detection results:",[200,448,450],{"className":202,"code":449,"language":204,"meta":205,"style":205},"[\n  {\n    \"label\": \"dog\",\n    \"score\": 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640",[209,606,242],{"class":215},[209,608,609],{"class":238}," 520",[209,611,317],{"class":215},[209,613,615],{"class":211,"line":614},11,[209,616,617],{"class":215},"  }\n",[209,619,621],{"class":211,"line":620},12,[209,622,317],{"class":215},[14,624,625],{},"Each object includes:",[51,627,628,634,640],{},[54,629,630,633],{},[18,631,632],{},"label:"," Detected class name (for example, dog, person, car)",[54,635,636,639],{},[18,637,638],{},"score:"," Confidence score for the detection",[54,641,642,645,646],{},[18,643,644],{},"bbox:"," Bounding box coordinates ",[61,647,648],{},"[x_min, y_min, x_max, y_max]",[376,650,652],{"id":651},"when-not-supported-raw-output","When Not Supported (Raw Output)",[14,654,655],{},"If the node cannot interpret the model output automatically, it returns the raw response:",[200,657,659],{"className":202,"code":658,"language":204,"meta":205,"style":205},"{\n  \"result\": [...],\n  \"labels\": {\n    \"0\": \"person\",\n    \"1\": \"bicycle\",\n    \"2\": 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can then use a ",[18,767,768],{},"Function"," node for custom post-processing.",[41,771,773],{"id":772},"notes","Notes",[51,775,776,784,791,802],{},[54,777,778,779,28,781,783],{},"The node currently supports ",[18,780,27],{},[18,782,31],{},"-style object detection models.",[54,785,786,787,790],{},"YOLO models currently accept ",[18,788,789],{},"only single-image input"," (batching support will be added in future releases).",[54,792,793,794,797,798,801],{},"Ensure that your model is compatible with ",[18,795,796],{},"ONNX Runtime"," and designed for ",[18,799,800],{},"object detection"," tasks.",[54,803,804,805,808,809,39],{},"For improved performance on devices with limited resources, use ",[18,806,807],{},"quantized models"," such as ",[61,810,111],{},[41,812,814],{"id":813},"example-flow","Example Flow",[816,817,818],"render-flow",{},[200,819,821],{"className":202,"code":820,"language":204,"meta":205,"style":205},"[{\"id\":\"6a3ca8414acfad34\",\"type\":\"http 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