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libTRT\n\nThis library performs non-blocking inference on tensorRT model.\n\n## Features\n\n- Support TensorRT engine model\n- Support ONNX model\n- Include yolo post processing\n\n## Usage\n\n```js\n\nvar model = libTRT()\nawait model.load(\"yolov5x.engine\") // File generated for the target GPU from a .wts file (see https://github.com/ultralytics/yolov5/releases)\n\nconsole.log(model.info()) // Model inspection\n\nvar input = {}\ninput['data'] = { // input name in model\n  \"dim\": [3, 640, 640], // Used as a validation of the input\n  \"data\": arrImgs, // Float32Array, OR Array of Float32Array with length not higher than the maximum batch size of the model\n}\n\nvar out = await model.execute(input) // Return an object with outputs as keys\n\nvar objs = model.yolo({ // This library includes a yolo post processing function\n  data: out['prob'].data, // OR .data[i] if batch is used\n  width: 640,\n  height: 640,\n  input_size: 640, // or 1280 depending on your model \n  conf_thresh: 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