tensorrt_demos:TensorRT YOLOv4，YOLOv3，SSD，MTCNN和GoogLeNet
tensorrt_demos
展示如何使用TensorRT优化caffe / tensorflow / darknet模型并在NVIDIA Jetson或x86_64 PC平台上运行推理的示例。
在Jetson Nano上以约4.6 FPS运行优化的“ yolov4-416”物体检测器。
在Jetson Nano上以约4.9 FPS的速度运行优化的“ yolov3-416”物体检测器。
在Jetson Nano上以27〜28 FPS运行优化的“ ssd_mobilenet_v1_coco”对象检测器（“ trt_ssd_async.py”）。
在Jetson Nano上以6〜11 FPS运行非常精确的优化“ MTCNN”面部检测器。
在Jetson Nano上以“每张图像〜16毫秒（仅供参考）”运行优化的“ GoogLeNet”图像分类器。
除了Jetson Nano，所有演
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