ultralytics 8.0.235 YOLOv8 OBB train, val, predict and export (#4499)
Co-authored-by: Yash Khurana <ykhurana6@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Swamita Gupta <swamita2001@gmail.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Laughing-q <1182102784@qq.com>
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---
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comments: true
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description: Learn about the cornerstone computer vision tasks YOLOv8 can perform including detection, segmentation, classification, and pose estimation. Understand their uses in your AI projects.
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keywords: Ultralytics, YOLOv8, Detection, Segmentation, Classification, Pose Estimation, AI Framework, Computer Vision Tasks
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keywords: Ultralytics, YOLOv8, Detection, Segmentation, Classification, Pose Estimation, Oriented Object Detection, AI Framework, Computer Vision Tasks
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---
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# Ultralytics YOLOv8 Tasks
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@ -9,7 +9,7 @@ keywords: Ultralytics, YOLOv8, Detection, Segmentation, Classification, Pose Est
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<br>
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<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
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YOLOv8 is an AI framework that supports multiple computer vision **tasks**. The framework can be used to perform [detection](detect.md), [segmentation](segment.md), [classification](classify.md), and [pose](pose.md) estimation. Each of these tasks has a different objective and use case.
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YOLOv8 is an AI framework that supports multiple computer vision **tasks**. The framework can be used to perform [detection](detect.md), [segmentation](segment.md), [obb](obb.md), [classification](classify.md), and [pose](pose.md) estimation. Each of these tasks has a different objective and use case.
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<p align="center">
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<br>
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@ -19,7 +19,7 @@ YOLOv8 is an AI framework that supports multiple computer vision **tasks**. The
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> Explore Ultralytics YOLO Tasks: Object Detection, Segmentation, Tracking, and Pose Estimation.
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<strong>Watch:</strong> Explore Ultralytics YOLO Tasks: Object Detection, Segmentation, OBB, Tracking, and Pose Estimation.
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</p>
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## [Detection](detect.md)
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@ -46,6 +46,12 @@ Pose/keypoint detection is a task that involves detecting specific points in an
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[Pose Examples](pose.md){ .md-button }
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## [Obb](obb.md)
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Oriented object detection goes a step further than regular object detection with introducing an extra angle to locate objects more accurate in an image. YOLOv8 can detect rotated objects in an image or video frame with high accuracy and speed.
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[Oriented Detection](obb.md){ .md-button }
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## Conclusion
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YOLOv8 supports multiple tasks, including detection, segmentation, classification, and keypoints detection. Each of these tasks has different objectives and use cases. By understanding the differences between these tasks, you can choose the appropriate task for your computer vision application.
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YOLOv8 supports multiple tasks, including detection, segmentation, classification, oriented object detection and keypoints detection. Each of these tasks has different objectives and use cases. By understanding the differences between these tasks, you can choose the appropriate task for your computer vision application.
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docs/en/tasks/obb.md
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docs/en/tasks/obb.md
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---
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comments: true
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description: Learn how to use oriented object detection models with Ultralytics YOLO. Instructions on training, validation, image prediction, and model export.
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keywords: yolov8, oriented object detection, Ultralytics, DOTA dataset, rotated object detection, object detection, model training, model validation, image prediction, model export
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---
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# Oriented Object Detection
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<!-- obb task poster -->
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Oriented object detection goes a step further than object detection and introduce an extra angle to locate objects more accurate in an image.
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The output of an oriented object detector is a set of rotated bounding boxes that exactly 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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<!-- youtube video link for obb task -->
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!!! Tip "Tip"
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YOLOv8 Obb models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml).
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## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)
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YOLOv8 pretrained Obb models are shown here, which are pretrained on the [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.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>box<br>50 | 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-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-obb.pt) | 1024 | <++> | <++> | <++> | 3.2 | 23.3 |
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| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-obb.pt) | 1024 | <++> | <++> | <++> | 11.4 | 76.3 |
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| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-obb.pt) | 1024 | <++> | <++> | <++> | 26.4 | 208.6 |
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| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-obb.pt) | 1024 | <++> | <++> | <++> | 44.5 | 433.8 |
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| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-obb.pt) | 1024 | <++> | <++> | <++> | 69.5 | 676.7 |
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<!-- TODO: should we report multi-scale results only as they're better or both multi-scale and single-scale. -->
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- **mAP<sup>val</sup>** values are for single-model single-scale on [DOTAv1 test](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0`
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- **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
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## Train
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<!-- TODO: probably we should create a sample dataset like coco128.yaml, named dota128.yaml? -->
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Train YOLOv8n-obb on the dota128.yaml 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-obb.yaml') # build a new model from YAML
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model = YOLO('yolov8n-obb.pt') # load a pretrained model (recommended for training)
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model = YOLO('yolov8n-obb.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='dota128-obb.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 obb train data=dota128-obb.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
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# Start training from a pretrained *.pt model
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yolo obb train data=dota128-obb.yaml model=yolov8n-obb.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 obb train data=dota128-obb.yaml model=yolov8n-obb.yaml pretrained=yolov8n-obb.pt epochs=100 imgsz=640
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```
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### Dataset format
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yolo obb dataset format can be found in detail in the [Dataset Guide](../datasets/obb/index.md)..
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## Val
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Validate trained YOLOv8n-obb model accuracy on the dota128-obb dataset. No argument need to passed as the `model`
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retains it's 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-obb.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(B)
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metrics.box.map50 # map50(B)
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metrics.box.map75 # map75(B)
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metrics.box.maps # a list contains map50-95(B) of each category
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```
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=== "CLI"
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```bash
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yolo obb val model=yolov8n-obb.pt # val official model
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yolo obb 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-obb 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-obb.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 obb predict model=yolov8n-obb.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo obb 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](https://docs.ultralytics.com/modes/predict/) page.
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## Export
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Export a YOLOv8n-obb 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-obb.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-obb.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-obb export formats are in the table below. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-obb.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-obb.pt` | ✅ | - |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-obb.torchscript` | ✅ | `imgsz`, `optimize` |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-obb.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-obb_openvino_model/` | ✅ | `imgsz`, `half` |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-obb.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-obb.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-obb_saved_model/` | ✅ | `imgsz`, `keras` |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-obb.pb` | ❌ | `imgsz` |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-obb.tflite` | ✅ | `imgsz`, `half`, `int8` |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-obb_edgetpu.tflite` | ✅ | `imgsz` |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-obb_web_model/` | ✅ | `imgsz`, `half`, `int8` |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-obb_paddle_model/` | ✅ | `imgsz` |
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| [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-obb_ncnn_model/` | ✅ | `imgsz`, `half` |
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See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.
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