You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
94 lines
6.3 KiB
94 lines
6.3 KiB
--- |
|
comments: true |
|
description: Learn how to profile speed and accuracy of YOLOv8 across various export formats; get insights on mAP50-95, accuracy_top5 metrics, and more. |
|
keywords: Ultralytics, YOLOv8, benchmarking, speed profiling, accuracy profiling, mAP50-95, accuracy_top5, ONNX, OpenVINO, TensorRT, YOLO export formats |
|
--- |
|
|
|
# Model Benchmarking with Ultralytics YOLO |
|
|
|
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics YOLO ecosystem and integrations"> |
|
|
|
## 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](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | |
|
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` | |
|
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` | |
|
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8` | |
|
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` | |
|
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` | |
|
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8` | |
|
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` | |
|
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` | |
|
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` | |
|
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` | |
|
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` | |
|
| [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half` | |
|
|
|
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.
|
|
|