Row 58836

Row ID: 58836 | Dataset Entry | Axioma AXP Content Repository

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I quantized YOLOv8 in Jetson Orin Nano. I exported it with TensorRT (FP16, INT8) and compared the performance. Based on YOLOv8s, the mAP50-95 of the base model is 44.7 and the inference speed is 33.1 ms. The model exported with TensorRT (FP16) showed that mAP50-95 was 44.7 and the inference speed was 11.4 ms. The model exported with TensorRT (INT8) showed that mAP50-95 was 41.2 and the inference speed was 8.2 ms. There was a slight loss in mAP50-95, but the inference speed was drastically reduced. There was a problem with calibration by exporting it with TensorRT (INT8), but the loss of mAP50-95 was minimized by increasing the calibration data. I tested with all base models of YOLOv8 as well as YOLOv8s.

[https://github.com/the0807/YOLOv8-ONNX-TensorRT](https://github.com/the0807/YOLOv8-ONNX-TensorRT)

FieldValue
text I quantized YOLOv8 in Jetson Orin Nano. I exported it with TensorRT (FP16, INT8) and compared the performance. Based on YOLOv8s, the mAP50-95 of the base model is 44.7 and the inference speed is 33.1 ms. The model exported with TensorRT (FP16) showed that mAP50-95 was 44.7 and the inference speed was 11.4 ms. The model exported with TensorRT (INT8) showed that mAP50-95 was 41.2 and the inference speed was 8.2 ms. There was a slight loss in mAP50-95, but the inference speed was drastically reduce…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-23
username_encoded Z0FBQUFBQm5Lak1YV3NqT0g4MkZiQ3ZBN2VMVjZJdG1oeGVQNVRTYk1lT3hLMzdVelM2cFhSRnJMd3UwUVhPUUw5WmRiTHJublcwbTVsNFR1NHR4M3QwR0FDbVJiY0dNckE9PQ==
url_encoded Z0FBQUFBQm5Lak9uRUVrZFptSFY5cU1QZDM1d0FQdE9Fc0s3bHZCNHhfRGFJOXVBZUZONkhkMGlrMG9NMUdUUkZkN09iU24yUTRDd1ZhTGhDd1ZQVUprdUdEeWhsU0hCbWljN3kxM2cwZU05MzRnZ2hDVzBnQndwYXVEVjhQRllLbTlKU1QzWmFEQ3U4UVN0dGUyS25qTFNXVDg0QTdHd1BILVY4Sm5hMVJaLUFGN3YxWkpMT2Z2Vi1ydkVWLXVlRmlwZnhjN1J1TW9i

Raw Record

{
  "text": "I quantized YOLOv8 in Jetson Orin Nano. I exported it with TensorRT (FP16, INT8) and compared the performance. Based on YOLOv8s, the mAP50-95 of the base model is 44.7 and the inference speed is 33.1 ms. The model exported with TensorRT (FP16) showed that mAP50-95 was 44.7 and the inference speed was 11.4 ms. The model exported with TensorRT (INT8) showed that mAP50-95 was 41.2 and the inference speed was 8.2 ms. There was a slight loss in mAP50-95, but the inference speed was drastically reduced. There was a problem with calibration by exporting it with TensorRT (INT8), but the loss of mAP50-95 was minimized by increasing the calibration data. I tested with all base models of YOLOv8 as well as YOLOv8s.\n\n[https://github.com/the0807/YOLOv8-ONNX-TensorRT](https://github.com/the0807/YOLOv8-ONNX-TensorRT)",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-23",
  "username_encoded": "Z0FBQUFBQm5Lak1YV3NqT0g4MkZiQ3ZBN2VMVjZJdG1oeGVQNVRTYk1lT3hLMzdVelM2cFhSRnJMd3UwUVhPUUw5WmRiTHJublcwbTVsNFR1NHR4M3QwR0FDbVJiY0dNckE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9uRUVrZFptSFY5cU1QZDM1d0FQdE9Fc0s3bHZCNHhfRGFJOXVBZUZONkhkMGlrMG9NMUdUUkZkN09iU24yUTRDd1ZhTGhDd1ZQVUprdUdEeWhsU0hCbWljN3kxM2cwZU05MzRnZ2hDVzBnQndwYXVEVjhQRllLbTlKU1QzWmFEQ3U4UVN0dGUyS25qTFNXVDg0QTdHd1BILVY4Sm5hMVJaLUFGN3YxWkpMT2Z2Vi1ydkVWLXVlRmlwZnhjN1J1TW9i"
}
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