Object detection is a task that involves identifying the location and class of objects in an image or video stream. 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
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(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 | - **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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.
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](../yolov5/train_custom_data.md). To convert your existing dataset from other formats( like COCO, VOC 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 | |--------------------------------------------------------------------|-------------------|---------------------------|----------| | [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | | [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | | [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | | [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | | [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | | [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ | | [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | | [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | | [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | | [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | | [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.