YOLOv8-16bit/docs/modes/benchmark.md

comments	description	keywords
true	Learn how to profile speed and accuracy of YOLOv8 across various export formats; get insights on mAP50-95, accuracy_top5 metrics, and more.	Ultralytics, YOLOv8, benchmarking, speed profiling, accuracy profiling, mAP50-95, accuracy_top5, ONNX, OpenVINO, TensorRT, YOLO export formats

Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks provide information on the size of the exported format, its mAP50-95 metrics (for object detection, segmentation and pose) or accuracy_top5 metrics (for classification), and the inference time in milliseconds per image across various export formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for their specific use case based on their requirements for speed and accuracy.

!!! tip "Tip"

* Export to ONNX or OpenVINO for up to 3x CPU speedup.
* Export to TensorRT for up to 5x GPU speedup.

Usage Examples

Run YOLOv8n benchmarks on all supported export formats including ONNX, TensorRT etc. See Arguments section below for a full list of export arguments.

!!! example ""

=== "Python"

    ```python
    from ultralytics.utils.benchmarks import benchmark

    # Benchmark on GPU
    benchmark(model='yolov8n.pt', data='coco8.yaml', imgsz=640, half=False, device=0)
    ```
=== "CLI"

    ```bash
    yolo benchmark model=yolov8n.pt data='coco8.yaml' imgsz=640 half=False device=0
    ```

Arguments

Arguments such as model, data, imgsz, half, device, and hard_fail provide users with the flexibility to fine-tune the benchmarks to their specific needs and compare the performance of different export formats with ease.

Key	Value	Description
model	None	path to model file, i.e. yolov8n.pt, yolov8n.yaml
data	None	path to yaml referencing the benchmarking dataset (under val label)
imgsz	640	image size as scalar or (h, w) list, i.e. (640, 480)
half	False	FP16 quantization
int8	False	INT8 quantization
device	None	device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
hard_fail	False	do not continue on error (bool), or val floor threshold (float)

Export Formats

Benchmarks will attempt to run automatically on all possible export formats below.

Format	format Argument	Model	Metadata
PyTorch	-	yolov8n.pt	✅
TorchScript	torchscript	yolov8n.torchscript	✅
ONNX	onnx	yolov8n.onnx	✅
OpenVINO	openvino	yolov8n_openvino_model/	✅
TensorRT	engine	yolov8n.engine	✅
CoreML	coreml	yolov8n.mlmodel	✅
TF SavedModel	saved_model	yolov8n_saved_model/	✅
TF GraphDef	pb	yolov8n.pb	❌
TF Lite	tflite	yolov8n.tflite	✅
TF Edge TPU	edgetpu	yolov8n_edgetpu.tflite	✅
TF.js	tfjs	yolov8n_web_model/	✅
PaddlePaddle	paddle	yolov8n_paddle_model/	✅
ncnn	ncnn	yolov8n_ncnn_model/	✅

See full export details in the Export page.