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Use macros for Docs tables de-duplication (#14990)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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17
docs/en/tasks/classify.md
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@ -2,6 +2,7 @@
comments: true
description: Master image classification using YOLOv8. Learn to train, validate, predict, and export models efficiently.
keywords: YOLOv8, image classification, AI, machine learning, pretrained models, ImageNet, model export, predict, train, validate
model_name: yolov8n-cls
---
# Image Classification
@ -165,21 +166,7 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
Available YOLOv8-cls 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-cls.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments |
| ------------------------------------------------- | ----------------- | ----------------------------- | -------- | -------------------------------------------------------------------- |
| [PyTorch](https://pytorch.org/) | - | `yolov8n-cls.pt` | ✅ | - |
| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-cls.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n-cls.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-cls_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch`, `dynamic` |
| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-cls.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-cls.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-cls_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-cls.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-cls.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-cls_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-cls_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n-cls_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
{% include "macros/export-table.md" %}
See full `export` details in the [Export](../modes/export.md) page.

16
docs/en/tasks/detect.md
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@ -167,21 +167,7 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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.
| Format | `format` Argument | Model | Metadata | Arguments |
| ------------------------------------------------- | ----------------- | ------------------------- | -------- | -------------------------------------------------------------------- |
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch`, `dynamic` |
| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
{% include "macros/export-table.md" %}
See full `export` details in the [Export](../modes/export.md) page.

17
docs/en/tasks/obb.md
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@ -2,6 +2,7 @@
comments: true
description: Discover how to detect objects with rotation for higher precision using YOLOv8 OBB models. Learn, train, validate, and export OBB models effortlessly.
keywords: Oriented Bounding Boxes, OBB, Object Detection, YOLOv8, Ultralytics, DOTAv1, Model Training, Model Export, AI, Machine Learning
model_name: yolov8n-obb
---
# Oriented Bounding Boxes Object Detection
@ -188,21 +189,7 @@ Export a YOLOv8n-obb model to a different format like ONNX, CoreML, etc.
Available YOLOv8-obb 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-obb.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments |
| ------------------------------------------------- | ----------------- | ----------------------------- | -------- | -------------------------------------------------------------------- |
| [PyTorch](https://pytorch.org/) | - | `yolov8n-obb.pt` | ✅ | - |
| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-obb.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n-obb.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-obb_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch`, `dynamic` |
| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-obb.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-obb.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-obb_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-obb.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-obb.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-obb_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-obb_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-obb_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n-obb_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
{% include "macros/export-table.md" %}
See full `export` details in the [Export](../modes/export.md) page.

17
docs/en/tasks/pose.md
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@ -2,6 +2,7 @@
comments: true
description: Discover how to use YOLOv8 for pose estimation tasks. Learn about model training, validation, prediction, and exporting in various formats.
keywords: pose estimation, YOLOv8, Ultralytics, keypoints, model training, image recognition, deep learning
model_name: yolov8n-pose
---
# Pose Estimation
@ -201,21 +202,7 @@ Export a YOLOv8n Pose model to a different format like ONNX, CoreML, etc.
Available YOLOv8-pose 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-pose.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments |
| ------------------------------------------------- | ----------------- | ------------------------------ | -------- | -------------------------------------------------------------------- |
| [PyTorch](https://pytorch.org/) | - | `yolov8n-pose.pt` | ✅ | - |
| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-pose.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n-pose.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-pose_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch`, `dynamic` |
| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-pose.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-pose.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-pose_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-pose.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-pose.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-pose_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-pose_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n-pose_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
{% include "macros/export-table.md" %}
See full `export` details in the [Export](../modes/export.md) page.

17
docs/en/tasks/segment.md
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@ -2,6 +2,7 @@
comments: true
description: Master instance segmentation using YOLOv8. Learn how to detect, segment and outline objects in images with detailed guides and examples.
keywords: instance segmentation, YOLOv8, object detection, image segmentation, machine learning, deep learning, computer vision, COCO dataset, Ultralytics
model_name: yolov8n-seg
---
# Instance Segmentation
@ -172,21 +173,7 @@ Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
Available YOLOv8-seg 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-seg.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments |
| ------------------------------------------------- | ----------------- | ----------------------------- | -------- | -------------------------------------------------------------------- |
| [PyTorch](https://pytorch.org/) | - | `yolov8n-seg.pt` | ✅ | - |
| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-seg.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n-seg.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-seg_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch`, `dynamic` |
| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-seg.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-seg.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-seg_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-seg.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-seg.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-seg_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-seg_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n-seg_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
{% include "macros/export-table.md" %}
See full `export` details in the [Export](../modes/export.md) page.