Does YOLOv8 classification support FP16 and INT8? about ultralytics

[glenn-jocher]

Hello! 😊

Exporting your custom YOLOv8 classification model to TensorRT (INT8) involves a few key steps:

1. Prepare Calibration Dataset: Yes, you'll need a representative calibration dataset. This dataset helps TensorRT determine optimal scaling factors for INT8 quantization to maintain accuracy.

2. Export the Model: Use the export function with the format='engine', int8=True, and specify your calibration dataset using the data argument.

3. Calibration Process: During export, TensorRT will perform the calibration using the provided dataset to optimize the model for INT8 precision.

That's it! These steps will convert your model to the TensorRT format optimized for INT8 operations. If you have any more questions or need further assistance, feel free to ask. Happy modeling! 🚀

[glenn-jocher]

Hello! 😊

Thank you for your question and for providing detailed information about your experience with exporting the INT8 TensorRT model.

To address your concerns:

1. INT8 Quantization: When exporting a model with int8=True, TensorRT performs post-training quantization, which can lead to differences in evaluation results compared to FP32 models. This is expected due to the reduced precision. Ensure you have a representative calibration dataset specified using the data argument during export to achieve optimal performance.

2. FP16 Quantization: Using half=True converts the model to FP16 precision, which can also result in different evaluation metrics compared to FP32. This is because FP16 has lower precision than FP32 but offers faster inference speeds.

3. YOLOv8 Classification Support: Yes, YOLOv8 classification models do support both FP16 and INT8 quantization. You can export your PyTorch model (yolov8n.pt) to TensorRT with these quantizations using the following commands:

   from ultralytics import YOLO
   
   model = YOLO("yolov8n.pt")
   model.export(format="engine", int8=True, data="path/to/calibration_dataset.yaml")
   model.export(format="engine", half=True)

4. Validation Differences: The yolo val detect function should reflect the quantization differences. If you observe no difference, ensure you are using the latest version of the Ultralytics package and that the calibration dataset is correctly specified for INT8 quantization.

If the issue persists, please provide a minimum reproducible example to help us diagnose the problem more effectively. You can find guidance on creating a reproducible example here.

Feel free to reach out if you have any more questions. Happy modeling! 🚀

[glenn-jocher]

Yes, YOLOv8 classification models do support FP16 and INT8 quantization. You can convert your trained FP32 model to these formats using the export mode with the appropriate arguments (half=True for FP16 and int8=True for INT8). This conversion can help optimize your model for faster inference speeds and reduced model size, suitable for deployment on platforms with limited computational resources.

If you need further guidance on how to perform these conversions, please refer to the export section of our documentation.

[pornpra]

@glenn-jocher Thanks for your answers :)

To export a trained custom YOLOv8 classification model from PyTorch format (FP32) to TensorRT or engine format (INT8), how many steps are involved in the export process, and is it necessary to prepare a calibration dataset for use during the calibration process with TensorRT?

[yunlongwangsd]

Hello, I've tried to export an INT8 TensorRT model using the above method, and the evaluation result is different from the TensorRT model without adding int8=True. However, when using yolo val detect function, there is no difference whether adding int8=True or not, but the result is different when adding half=True. I am wondering if YOLOv8 classification supports any quantization for default PyTorch model (yolov8n.pt) when using int8 and half arguments. Any help would be appreciated!