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294 lines
15 KiB
294 lines
15 KiB
--- |
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comments: true |
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description: Learn about object detection with YOLOv8. Explore pretrained models, training, validation, prediction, and export details for efficient object recognition. |
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keywords: object detection, YOLOv8, pretrained models, training, validation, prediction, export, machine learning, computer vision |
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--- |
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# Object Detection |
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<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418624-5785cb93-74c9-4541-9179-d5c6782d491a.png" alt="Object detection examples"> |
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Object detection is a task that involves identifying the location and class of objects in an image or video stream. |
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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. |
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<p align="center"> |
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<br> |
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/5ku7npMrW40?si=6HQO1dDXunV8gekh" |
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title="YouTube video player" frameborder="0" |
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
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allowfullscreen> |
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</iframe> |
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<br> |
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<strong>Watch:</strong> Object Detection with Pre-trained Ultralytics YOLOv8 Model. |
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</p> |
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!!! Tip "Tip" |
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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/cfg/datasets/coco.yaml). |
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## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8) |
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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/cfg/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset. |
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use. |
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| 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) | |
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| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | |
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| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 | |
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| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 | |
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| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 | |
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 | |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 | |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0` |
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- **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=coco8.yaml batch=1 device=0|cpu` |
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## Train |
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Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page. |
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!!! Example |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO("yolov8n.yaml") # build a new model from YAML |
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training) |
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model = YOLO("yolov8n.yaml").load("yolov8n.pt") # build from YAML and transfer weights |
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# Train the model |
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results = model.train(data="coco8.yaml", epochs=100, imgsz=640) |
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``` |
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=== "CLI" |
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```bash |
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# Build a new model from YAML and start training from scratch |
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yolo detect train data=coco8.yaml model=yolov8n.yaml epochs=100 imgsz=640 |
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# Start training from a pretrained *.pt model |
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yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640 |
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# Build a new model from YAML, transfer pretrained weights to it and start training |
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yolo detect train data=coco8.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640 |
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``` |
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### Dataset format |
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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](https://github.com/ultralytics/JSON2YOLO) tool by Ultralytics. |
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## Val |
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Validate trained YOLOv8n model accuracy on the COCO8 dataset. No argument need to passed as the `model` retains its training `data` and arguments as model attributes. |
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!!! Example |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO("yolov8n.pt") # load an official model |
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model = YOLO("path/to/best.pt") # load a custom model |
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# Validate the model |
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metrics = model.val() # no arguments needed, dataset and settings remembered |
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metrics.box.map # map50-95 |
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metrics.box.map50 # map50 |
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metrics.box.map75 # map75 |
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metrics.box.maps # a list contains map50-95 of each category |
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``` |
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=== "CLI" |
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```bash |
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yolo detect val model=yolov8n.pt # val official model |
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yolo detect val model=path/to/best.pt # val custom model |
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``` |
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## Predict |
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Use a trained YOLOv8n model to run predictions on images. |
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!!! Example |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO("yolov8n.pt") # load an official model |
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model = YOLO("path/to/best.pt") # load a custom model |
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# Predict with the model |
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image |
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``` |
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=== "CLI" |
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```bash |
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yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model |
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yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model |
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``` |
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See full `predict` mode details in the [Predict](../modes/predict.md) page. |
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## Export |
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Export a YOLOv8n model to a different format like ONNX, CoreML, etc. |
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!!! Example |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO("yolov8n.pt") # load an official model |
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model = YOLO("path/to/best.pt") # load a custom trained model |
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# Export the model |
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model.export(format="onnx") |
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``` |
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=== "CLI" |
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```bash |
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yolo export model=yolov8n.pt format=onnx # export official model |
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yolo export model=path/to/best.pt format=onnx # export custom trained model |
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``` |
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Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. 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. |
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| Format | `format` Argument | Model | Metadata | Arguments | |
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| ------------------------------------------------- | ----------------- | ------------------------- | -------- | -------------------------------------------------------------------- | |
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | |
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| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | |
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| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` | |
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| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | |
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| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` | |
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| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | |
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| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | |
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| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | |
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| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | |
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| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` | |
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| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | |
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| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | |
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| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | |
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See full `export` details in the [Export](../modes/export.md) page. |
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## FAQ |
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### How do I train a YOLOv8 model on my custom dataset? |
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Training a YOLOv8 model on a custom dataset involves a few steps: |
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1. **Prepare the Dataset**: Ensure your dataset is in the YOLO format. For guidance, refer to our [Dataset Guide](../datasets/detect/index.md). |
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2. **Load the Model**: Use the Ultralytics YOLO library to load a pre-trained model or create a new model from a YAML file. |
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3. **Train the Model**: Execute the `train` method in Python or the `yolo detect train` command in CLI. |
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!!! Example |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a pretrained model |
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model = YOLO("yolov8n.pt") |
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# Train the model on your custom dataset |
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model.train(data="my_custom_dataset.yaml", epochs=100, imgsz=640) |
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``` |
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=== "CLI" |
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```bash |
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yolo detect train data=my_custom_dataset.yaml model=yolov8n.pt epochs=100 imgsz=640 |
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``` |
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For detailed configuration options, visit the [Configuration](../usage/cfg.md) page. |
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### What pretrained models are available in YOLOv8? |
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Ultralytics YOLOv8 offers various pretrained models for object detection, segmentation, and pose estimation. These models are pretrained on the COCO dataset or ImageNet for classification tasks. Here are some of the available models: |
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- [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) |
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- [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) |
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- [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) |
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- [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) |
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- [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) |
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For a detailed list and performance metrics, refer to the [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8) section. |
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### How can I validate the accuracy of my trained YOLOv8 model? |
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To validate the accuracy of your trained YOLOv8 model, you can use the `.val()` method in Python or the `yolo detect val` command in CLI. This will provide metrics like mAP50-95, mAP50, and more. |
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!!! Example |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load the model |
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model = YOLO("path/to/best.pt") |
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# Validate the model |
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metrics = model.val() |
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print(metrics.box.map) # mAP50-95 |
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``` |
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=== "CLI" |
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```bash |
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yolo detect val model=path/to/best.pt |
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``` |
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For more validation details, visit the [Val](../modes/val.md) page. |
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### What formats can I export a YOLOv8 model to? |
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Ultralytics YOLOv8 allows exporting models to various formats such as ONNX, TensorRT, CoreML, and more to ensure compatibility across different platforms and devices. |
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!!! Example |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load the model |
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model = YOLO("yolov8n.pt") |
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# Export the model to ONNX format |
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model.export(format="onnx") |
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``` |
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=== "CLI" |
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```bash |
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yolo export model=yolov8n.pt format=onnx |
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``` |
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Check the full list of supported formats and instructions on the [Export](../modes/export.md) page. |
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### Why should I use Ultralytics YOLOv8 for object detection? |
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Ultralytics YOLOv8 is designed to offer state-of-the-art performance for object detection, segmentation, and pose estimation. Here are some key advantages: |
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1. **Pretrained Models**: Utilize models pretrained on popular datasets like COCO and ImageNet for faster development. |
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2. **High Accuracy**: Achieves impressive mAP scores, ensuring reliable object detection. |
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3. **Speed**: Optimized for real-time inference, making it ideal for applications requiring swift processing. |
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4. **Flexibility**: Export models to various formats like ONNX and TensorRT for deployment across multiple platforms. |
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Explore our [Blog](https://www.ultralytics.com/blog) for use cases and success stories showcasing YOLOv8 in action.
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