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--- |
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pipeline_tag: object-detection |
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datasets: |
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- Mai0313/coco-pose-2017 |
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tags: |
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- TensorRT |
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- Pose Estimation |
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- YOLO-NAS-Pose |
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- Jetson Orin |
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--- |
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We offer a TensorRT model in various precisions including int8, fp16, fp32, and mixed, converted from Deci-AI's [YOLO-NAS-Pose](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS-POSE.md) pre-trained weights in PyTorch. |
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This model is compatible with Jetson Orin Nano hardware. |
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Note that all quantization that has been introduced in the conversion is purely static, meaning that the corresponding model has potentillay bad accuracy compared to the original one. |
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Todo: use [coco-pose-2017](https://huggingface.co/datasets/Mai0313/coco-pose-2017) dataset to calibrate int8 model |
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More information on calibration for post-training quantization, check [this slide](https://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf) |
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# Large |
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| Model Name | ONNX Precision | TensorRT Preicion | Throughput (TensorRT) | |
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|---|---|---|---| |
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| yolo_nas_pose_l_fp16.onnx.best.engine | FP16 | FP32+FP16+INT8 | 46.7231 qps | |
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| yolo_nas_pose_l_fp16.onnx.fp16.engine | FP16 | FP32+FP16 | 29.6093 qps | |
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| yolo_nas_pose_l_fp32.onnx.best.engine | FP32 | FP32+FP16+INT8 | 47.4032 qps | |
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| yolo_nas_pose_l_fp32.onnx.engine | FP32 | FP32 | 15.0654 qps | |
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| yolo_nas_pose_l_fp32.onnx.fp16.engine | FP32 | FP32+FP16 | 29.0005 qps | |
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| yolo_nas_pose_l_fp32.onnx.int8.engine | FP32 | FP32+INT8 | 47.9071 qps | |
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| yolo_nas_pose_l_int8.onnx.best.engine | INT8 | FP32+FP16+INT8 | 36.9695 qps | |
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| yolo_nas_pose_l_int8.onnx.int8.engine | INT8 | FP32+INT8 | 30.9676 qps | |
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# Medium |
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| Model Name | ONNX Precision | TensorRT Preicion | Throughput (TensorRT) | |
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| yolo_nas_pose_m_fp16.onnx.best.engine | FP16 | FP32+FP16+INT8 | 58.254 qps | |
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| yolo_nas_pose_m_fp16.onnx.fp16.engine | FP16 | FP32+FP16 | 37.8547 qps | |
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| yolo_nas_pose_m_fp32.onnx.best.engine | FP32 | FP32+FP16+INT8 | 58.0306 qps | |
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| yolo_nas_pose_m_fp32.onnx.engine | FP32 | FP32 | 18.9603 qps | |
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| yolo_nas_pose_m_fp32.onnx.fp16.engine | FP32 | FP32+FP16 | 37.193 qps | |
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| yolo_nas_pose_m_fp32.onnx.int8.engine | FP32 | FP32+INT8 | 59.9746 qps | |
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| yolo_nas_pose_m_int8.onnx.best.engine | INT8 | FP32+FP16+INT8 | 44.8046 qps | |
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| yolo_nas_pose_m_int8.onnx.int8.engine | INT8 | FP32+INT8 | 38.6757 qps | |
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# Small |
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| Model Name | ONNX Precision | TensorRT Preicion | Throughput (TensorRT) | |
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|---|---|---|---| |
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| yolo_nas_pose_s_fp16.onnx.best.engine | FP16 | FP32+FP16+INT8 |84.7072 qps| |
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| yolo_nas_pose_s_fp16.onnx.fp16.engine | FP16 | FP32+FP16 | 66.0151 qps | |
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| yolo_nas_pose_s_fp32.onnx.best.engine | FP32 | FP32+FP16+INT8 | 85.5718 qps | |
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| yolo_nas_pose_s_fp32.onnx.engine | FP32 | FP32 | 33.5963 qps | |
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| yolo_nas_pose_s_fp32.onnx.fp16.engine | FP32 | FP32+FP16 | 65.4357 qps | |
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| yolo_nas_pose_s_fp32.onnx.int8.engine | FP32 | FP32+INT8 | 86.3202 qps| |
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| yolo_nas_pose_s_int8.onnx.best.engine | INT8 | FP32+FP16+INT8 | 74.2494 qps | |
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| yolo_nas_pose_s_int8.onnx.int8.engine | INT8 | FP32+INT8 | 63.7546 qps | |
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# Nano |
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| Model Name | ONNX Precision | TensorRT Preicion | Throughput (TensorRT) | |
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| yolo_nas_pose_n_fp16.onnx.best.engine | FP16 | FP32+FP16+INT8 | 91.8287 qps | |
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| yolo_nas_pose_n_fp16.onnx.fp16.engine | FP16 | FP32+FP16 | 85.4187 qps| |
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| yolo_nas_pose_n_fp32.onnx.best.engine | FP32 | FP32+FP16+INT8 | 105.519 qps| |
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| yolo_nas_pose_n_fp32.onnx.engine | FP32 | FP32 | 47.8265 qps | |
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| yolo_nas_pose_n_fp32.onnx.fp16.engine | FP32 | FP32+FP16 | 82.3834 qps| |
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| yolo_nas_pose_n_fp32.onnx.int8.engine | FP32 | FP32+INT8 | 88.0719 qps | |
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| yolo_nas_pose_n_int8.onnx.best.engine | INT8 | FP32+FP16+INT8 | 80.8271 qps | |
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| yolo_nas_pose_n_int8.onnx.int8.engine | INT8 | FP32+INT8 | 74.2658 qps | |
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