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.
172 lines
11 KiB
172 lines
11 KiB
---
|
|
comments: true
|
|
description: Learn how to use YOLOv8, an object detection model pre-trained with COCO and about the different YOLOv8 models and how to train and export them.
|
|
keywords: object detection, YOLOv8 Detect models, COCO dataset, models, train, predict, export
|
|
---
|
|
|
|
Object detection is a task that involves identifying the location and class of objects in an image or video stream.
|
|
|
|
<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418624-5785cb93-74c9-4541-9179-d5c6782d491a.png">
|
|
|
|
The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene, but don't need to know exactly where the object is or its exact shape.
|
|
|
|
!!! tip "Tip"
|
|
|
|
YOLOv8 Detect models are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml).
|
|
|
|
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8)
|
|
|
|
YOLOv8 pretrained Detect models are shown here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
|
|
|
|
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
|
|
|
|
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|
|
|--------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
|
|
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
|
|
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
|
|
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
|
|
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
|
|
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
|
|
|
|
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
|
|
<br>Reproduce by `yolo val detect data=coco.yaml device=0`
|
|
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
|
|
instance.
|
|
<br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
|
|
|
|
## Train
|
|
|
|
Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
|
|
|
|
!!! example ""
|
|
|
|
=== "Python"
|
|
|
|
```python
|
|
from ultralytics import YOLO
|
|
|
|
# Load a model
|
|
model = YOLO('yolov8n.yaml') # build a new model from YAML
|
|
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
|
|
model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
|
|
|
|
# Train the model
|
|
model.train(data='coco128.yaml', epochs=100, imgsz=640)
|
|
```
|
|
=== "CLI"
|
|
|
|
```bash
|
|
# Build a new model from YAML and start training from scratch
|
|
yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640
|
|
|
|
# Start training from a pretrained *.pt model
|
|
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
|
|
|
|
# Build a new model from YAML, transfer pretrained weights to it and start training
|
|
yolo detect train data=coco128.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
|
|
```
|
|
|
|
### Dataset format
|
|
|
|
YOLO detection dataset format can be found in detail in the [Dataset Guide](../datasets/detect/index.md). To convert your existing dataset from other formats( like COCO etc.) to YOLO format, please use [json2yolo tool](https://github.com/ultralytics/JSON2YOLO) by Ultralytics.
|
|
|
|
## Val
|
|
|
|
Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes.
|
|
|
|
!!! example ""
|
|
|
|
=== "Python"
|
|
|
|
```python
|
|
from ultralytics import YOLO
|
|
|
|
# Load a model
|
|
model = YOLO('yolov8n.pt') # load an official model
|
|
model = YOLO('path/to/best.pt') # load a custom model
|
|
|
|
# Validate the model
|
|
metrics = model.val() # no arguments needed, dataset and settings remembered
|
|
metrics.box.map # map50-95
|
|
metrics.box.map50 # map50
|
|
metrics.box.map75 # map75
|
|
metrics.box.maps # a list contains map50-95 of each category
|
|
```
|
|
=== "CLI"
|
|
|
|
```bash
|
|
yolo detect val model=yolov8n.pt # val official model
|
|
yolo detect val model=path/to/best.pt # val custom model
|
|
```
|
|
|
|
## Predict
|
|
|
|
Use a trained YOLOv8n model to run predictions on images.
|
|
|
|
!!! example ""
|
|
|
|
=== "Python"
|
|
|
|
```python
|
|
from ultralytics import YOLO
|
|
|
|
# Load a model
|
|
model = YOLO('yolov8n.pt') # load an official model
|
|
model = YOLO('path/to/best.pt') # load a custom model
|
|
|
|
# Predict with the model
|
|
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
|
|
```
|
|
=== "CLI"
|
|
|
|
```bash
|
|
yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
|
|
yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
|
|
```
|
|
|
|
See full `predict` mode details in the [Predict](https://docs.ultralytics.com/modes/predict/) page.
|
|
|
|
## Export
|
|
|
|
Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
|
|
|
|
!!! example ""
|
|
|
|
=== "Python"
|
|
|
|
```python
|
|
from ultralytics import YOLO
|
|
|
|
# Load a model
|
|
model = YOLO('yolov8n.pt') # load an official model
|
|
model = YOLO('path/to/best.pt') # load a custom trained
|
|
|
|
# Export the model
|
|
model.export(format='onnx')
|
|
```
|
|
=== "CLI"
|
|
|
|
```bash
|
|
yolo export model=yolov8n.pt format=onnx # export official model
|
|
yolo export model=path/to/best.pt format=onnx # export custom trained model
|
|
```
|
|
|
|
Available YOLOv8 export formats are in the table below. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n.onnx`. Usage examples are shown for your model after export completes.
|
|
|
|
| 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` |
|
|
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
|
|
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ | `imgsz`, `half`, `int8`, `nms` |
|
|
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` |
|
|
| [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. |