ultralytics/docs/en/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

Model Benchmarking with Ultralytics YOLO

Introduction

Once your model is trained and validated, the next logical step is to evaluate its performance in various real-world scenarios. Benchmark mode in Ultralytics YOLOv8 serves this purpose by providing a robust framework for assessing the speed and accuracy of your model across a range of export formats.

Why Is Benchmarking Crucial?

• Informed Decisions: Gain insights into the trade-offs between speed and accuracy.
• Resource Allocation: Understand how different export formats perform on different hardware.
• Optimization: Learn which export format offers the best performance for your specific use case.
• Cost Efficiency: Make more efficient use of hardware resources based on benchmark results.

Key Metrics in Benchmark Mode

• mAP50-95: For object detection, segmentation, and pose estimation.
• accuracy_top5: For image classification.
• Inference Time: Time taken for each image in milliseconds.

Supported Export Formats

• ONNX: For optimal CPU performance
• TensorRT: For maximal GPU efficiency
• OpenVINO: For Intel hardware optimization
• CoreML, TensorFlow SavedModel, and More: For diverse deployment needs.

!!! 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 verbose 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
verbose	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	Arguments
PyTorch	-	yolov8n.pt	✅	-
TorchScript	torchscript	yolov8n.torchscript	✅	imgsz, optimize
ONNX	onnx	yolov8n.onnx	✅	imgsz, half, dynamic, simplify, opset
OpenVINO	openvino	yolov8n_openvino_model/	✅	imgsz, half, int8
TensorRT	engine	yolov8n.engine	✅	imgsz, half, dynamic, simplify, workspace
CoreML	coreml	yolov8n.mlpackage	✅	imgsz, half, int8, nms
TF SavedModel	saved_model	yolov8n_saved_model/	✅	imgsz, keras, int8
TF GraphDef	pb	yolov8n.pb	❌	imgsz
TF Lite	tflite	yolov8n.tflite	✅	imgsz, half, int8
TF Edge TPU	edgetpu	yolov8n_edgetpu.tflite	✅	imgsz
TF.js	tfjs	yolov8n_web_model/	✅	imgsz
PaddlePaddle	paddle	yolov8n_paddle_model/	✅	imgsz
ncnn	ncnn	yolov8n_ncnn_model/	✅	imgsz, half

See full export details in the Export page.