Fast INT8 Inference for Autonomous Vehicles with TensorRT 3

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Originally published at: https://developer.nvidia.com/blog/int8-inference-autonomous-vehicles-tensorrt/

Autonomous driving demands safety, and a high-performance computing solution to process sensor data with extreme accuracy. Researchers and developers creating deep neural networks (DNNs) for self driving must optimize their networks to ensure low-latency inference and energy efficiency. Thanks to a new Python API in NVIDIA TensorRT, this process just became easier. Figure 1. TensorRT…

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Can you release the model?

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The blog is informative and helpful!
I have one question: how to write the read_calibration_cache() function? Could you please provide an example?

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What is the reduction of inference accuracy using INT8, the AI scientific circle are talking about upscale float point precision for AI not downscale.

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Could you please clarifying what should be the result for getLabels function for semantic segmentation? Thanks.

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getLabels() is only necessary for LegacyCalibrator. You don't need the score function and ground truth when using EntropyCalibrator.

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thank you for the blog, however ,may I ask have u ever tried the trt.lite.Engine() method with the parameter as "calibrator" to transfer to INT8, I tried but find the method is not supported now ,how did you make it?would you mind telling more details , many thanks!