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Add Export Arguments tables to all Export docs (#18952)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Francesco Mattioli <Francesco.mttl@gmail.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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11
docs/en/integrations/coreml.md
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@ -105,6 +105,17 @@ Before diving into the usage instructions, be sure to check out the range of [YO
yolo predict model=yolo11n.mlpackage source='https://ultralytics.com/images/bus.jpg' yolo predict model=yolo11n.mlpackage source='https://ultralytics.com/images/bus.jpg'
``` ```
### Export Arguments
| Argument | Type | Default | Description |
| -------- | ---------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `coreml` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
| `int8` | `bool` | `False` | Activates INT8 quantization, further compressing the model and speeding up inference with minimal [accuracy](https://www.ultralytics.com/glossary/accuracy) loss, primarily for edge devices. |
| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md). For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLO11 CoreML Models ## Deploying Exported YOLO11 CoreML Models

9
docs/en/integrations/edge-tpu.md
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@ -95,7 +95,14 @@ Before diving into the usage instructions, it's important to note that while all
yolo predict model=yolo11n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg' yolo predict model=yolo11n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
``` ```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md). ### Export Arguments
| Argument | Type | Default | Description |
| -------- | ---------------- | --------- | --------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `edgetpu` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLO11 TFLite Edge TPU Models ## Deploying Exported YOLO11 TFLite Edge TPU Models

12
docs/en/integrations/mnn.md
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@ -66,7 +66,17 @@ Before diving into the usage instructions, it's important to note that while all
yolo predict model='yolo11n.mnn' source='https://ultralytics.com/images/bus.jpg' yolo predict model='yolo11n.mnn' source='https://ultralytics.com/images/bus.jpg'
``` ```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md). ### Export Arguments
| Argument | Type | Default | Description |
| -------- | ---------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `mnn` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
| `int8` | `bool` | `False` | Activates INT8 quantization, further compressing the model and speeding up inference with minimal [accuracy](https://www.ultralytics.com/glossary/accuracy) loss, primarily for edge devices. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
### MNN-Only Inference ### MNN-Only Inference

11
docs/en/integrations/ncnn.md
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@ -97,7 +97,16 @@ Before diving into the usage instructions, it's important to note that while all
yolo predict model='./yolo11n_ncnn_model' source='https://ultralytics.com/images/bus.jpg' yolo predict model='./yolo11n_ncnn_model' source='https://ultralytics.com/images/bus.jpg'
``` ```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md). ### Export Arguments
| Argument | Type | Default | Description |
| -------- | ---------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `ncnn` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLO11 NCNN Models ## Deploying Exported YOLO11 NCNN Models

13
docs/en/integrations/onnx.md
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@ -113,6 +113,19 @@ Before diving into the usage instructions, be sure to check out the range of [YO
yolo predict model=yolo11n.onnx source='https://ultralytics.com/images/bus.jpg' yolo predict model=yolo11n.onnx source='https://ultralytics.com/images/bus.jpg'
``` ```
### Export Arguments
| Argument | Type | Default | Description |
| ---------- | ---------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `onnx` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
| `dynamic` | `bool` | `False` | Allows dynamic input sizes, enhancing flexibility in handling varying image dimensions. |
| `simplify` | `bool` | `True` | Simplifies the model graph with `onnxslim`, potentially improving performance and compatibility. |
| `opset` | `int` | `None` | Specifies the ONNX opset version for compatibility with different ONNX parsers and runtimes. If not set, uses the latest supported version. |
| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md). For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLO11 ONNX Models ## Deploying Exported YOLO11 ONNX Models

25
docs/en/integrations/openvino.md
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@ -57,21 +57,20 @@ Export a YOLOv8n model to OpenVINO format and run inference with the exported mo
yolo predict model=yolov8n_openvino_model source='https://ultralytics.com/images/bus.jpg' yolo predict model=yolov8n_openvino_model source='https://ultralytics.com/images/bus.jpg'
``` ```
## Arguments ## Export Arguments
| Key | Value | Description | | Argument | Type | Default | Description |
| --------- | ------------ | ------------------------------------------------------------------------------------------- | | --------- | ---------------- | ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `'openvino'` | format to export to | | `format` | `str` | `openvino` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | | `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `half` | `False` | FP16 quantization | | `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
| `int8` | `False` | INT8 quantization | | `int8` | `bool` | `False` | Activates INT8 quantization, further compressing the model and speeding up inference with minimal [accuracy](https://www.ultralytics.com/glossary/accuracy) loss, primarily for edge devices. |
| `batch` | `1` | [batch size](https://www.ultralytics.com/glossary/batch-size) for inference | | `dynamic` | `bool` | `False` | Allows dynamic input sizes, enhancing flexibility in handling varying image dimensions. |
| `dynamic` | `False` | allows dynamic input sizes | | `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
| `data` | `coco8.yaml` | Path to the dataset configuration file (default: `coco8.yaml`), essential for quantization. | | `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
| `data` | `str` | `coco8.yaml` | Path to the [dataset](https://docs.ultralytics.com/datasets) configuration file (default: `coco8.yaml`), essential for quantization. |
!!! note For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
When using `data` argument for quantization, please check [Dataset Guide](https://docs.ultralytics.com/datasets/detect) to learn more about the dataset format.
## Benefits of OpenVINO ## Benefits of OpenVINO

10
docs/en/integrations/paddlepaddle.md
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@ -110,7 +110,15 @@ Before diving into the usage instructions, it's important to note that while all
yolo predict model='./yolo11n_paddle_model' source='https://ultralytics.com/images/bus.jpg' yolo predict model='./yolo11n_paddle_model' source='https://ultralytics.com/images/bus.jpg'
``` ```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md). ### Export Arguments
| Argument | Type | Default | Description |
| -------- | ---------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `paddle` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLO11 PaddlePaddle Models ## Deploying Exported YOLO11 PaddlePaddle Models

9
docs/en/integrations/rockchip-rknn.md
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@ -91,6 +91,15 @@ For detailed instructions and best practices related to the installation process
yolo export model=yolo11n.pt format=rknn name=rk3588 # creates '/yolo11n_rknn_model' yolo export model=yolo11n.pt format=rknn name=rk3588 # creates '/yolo11n_rknn_model'
``` ```
### Export Arguments
| Argument | Type | Default | Description |
| -------- | ---------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `rknn` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
| `name` | `str` | `rk3588` | Specifies the Rockchip model (rk3588, rk3576, rk3566, rk3568, rk3562, rv1103, rv1106, rv1103b, rv1106b, rk2118) |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md). For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLO11 RKNN Models ## Deploying Exported YOLO11 RKNN Models

20
docs/en/integrations/sony-imx500.md
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@ -85,20 +85,16 @@ yolov8n_imx_model
└── yolov8n_imx500_model.pbtxt └── yolov8n_imx500_model.pbtxt
``` ```
## Arguments ## Export Arguments
When exporting a model to IMX500 format, you can specify various arguments: | Argument | Type | Default | Description |
| -------- | ---------------- | ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `imx` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `int8` | `bool` | `True` | Activates INT8 quantization, further compressing the model and speeding up inference with minimal [accuracy](https://www.ultralytics.com/glossary/accuracy) loss, primarily for edge devices. |
| `data` | `str` | `coco8.yaml` | Path to the [dataset](https://docs.ultralytics.com/datasets) configuration file (default: `coco8.yaml`), essential for quantization. |
| Key | Value | Description | For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
| -------- | ------------ | -------------------------------------------------------------- |
| `format` | `imx` | Format to export to (imx) |
| `int8` | `True` | Enable INT8 quantization for the model (default: `True`) |
| `imgsz` | `640` | Image size for the model input (default: `640`) |
| `data` | `coco8.yaml` | Path to the dataset configuration file (default: `coco8.yaml`) |
!!! note
When using `data` argument for quantization, please check [Dataset Guide](https://docs.ultralytics.com/datasets/detect) to learn more about the dataset format.
## Using IMX500 Export in Deployment ## Using IMX500 Export in Deployment

15
docs/en/integrations/tensorrt.md
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@ -107,6 +107,21 @@ Before diving into the usage instructions, be sure to check out the range of [YO
yolo predict model=yolo11n.engine source='https://ultralytics.com/images/bus.jpg' yolo predict model=yolo11n.engine source='https://ultralytics.com/images/bus.jpg'
``` ```
### Export Arguments
| Argument | Type | Default | Description |
| ----------- | ----------------- | ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `engine` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
| `int8` | `bool` | `False` | Activates INT8 quantization, further compressing the model and speeding up inference with minimal [accuracy](https://www.ultralytics.com/glossary/accuracy) loss, primarily for edge devices. |
| `dynamic` | `bool` | `False` | Allows dynamic input sizes, enhancing flexibility in handling varying image dimensions. |
| `simplify` | `bool` | `True` | Simplifies the model graph with `onnxslim`, potentially improving performance and compatibility. |
| `workspace` | `float` or `None` | `None` | Sets the maximum workspace size in GiB for TensorRT optimizations, balancing memory usage and performance; use `None` for auto-allocation by TensorRT up to device maximum. |
| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
| `data` | `str` | `coco8.yaml` | Path to the [dataset](https://docs.ultralytics.com/datasets) configuration file (default: `coco8.yaml`), essential for quantization. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md). For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
### Exporting TensorRT with INT8 Quantization ### Exporting TensorRT with INT8 Quantization

10
docs/en/integrations/tf-graphdef.md
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@ -103,7 +103,15 @@ Before diving into the usage instructions, it's important to note that while all
yolo predict model='yolo11n.pb' source='https://ultralytics.com/images/bus.jpg' yolo predict model='yolo11n.pb' source='https://ultralytics.com/images/bus.jpg'
``` ```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md). ### Export Arguments
| Argument | Type | Default | Description |
| -------- | ---------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `pb` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLO11 TF GraphDef Models ## Deploying Exported YOLO11 TF GraphDef Models

13
docs/en/integrations/tf-savedmodel.md
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@ -97,7 +97,18 @@ Before diving into the usage instructions, it's important to note that while all
yolo predict model='./yolo11n_saved_model' source='https://ultralytics.com/images/bus.jpg' yolo predict model='./yolo11n_saved_model' source='https://ultralytics.com/images/bus.jpg'
``` ```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md). ### Export Arguments
| Argument | Type | Default | Description |
| -------- | ---------------- | ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `saved_model` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `keras` | `bool` | `False` | Enables export to Keras format, providing compatibility with TensorFlow serving and APIs. |
| `int8` | `bool` | `False` | Activates INT8 quantization, further compressing the model and speeding up inference with minimal [accuracy](https://www.ultralytics.com/glossary/accuracy) loss, primarily for edge devices. |
| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLO11 TF SavedModel Models ## Deploying Exported YOLO11 TF SavedModel Models

13
docs/en/integrations/tfjs.md
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@ -95,7 +95,18 @@ Before diving into the usage instructions, it's important to note that while all
yolo predict model='./yolo11n_web_model' source='https://ultralytics.com/images/bus.jpg' yolo predict model='./yolo11n_web_model' source='https://ultralytics.com/images/bus.jpg'
``` ```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md). ### Export Arguments
| Argument | Type | Default | Description |
| -------- | ---------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `tfjs` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
| `int8` | `bool` | `False` | Activates INT8 quantization, further compressing the model and speeding up inference with minimal [accuracy](https://www.ultralytics.com/glossary/accuracy) loss, primarily for edge devices. |
| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLO11 TensorFlow.js Models ## Deploying Exported YOLO11 TensorFlow.js Models

12
docs/en/integrations/tflite.md
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@ -101,6 +101,18 @@ Before diving into the usage instructions, it's important to note that while all
yolo predict model='yolo11n_float32.tflite' source='https://ultralytics.com/images/bus.jpg' yolo predict model='yolo11n_float32.tflite' source='https://ultralytics.com/images/bus.jpg'
``` ```
### Export Arguments
| Argument | Type | Default | Description |
| -------- | ---------------- | ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `tflite` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
| `int8` | `bool` | `False` | Activates INT8 quantization, further compressing the model and speeding up inference with minimal [accuracy](https://www.ultralytics.com/glossary/accuracy) loss, primarily for edge devices. |
| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
| `data` | `str` | `coco8.yaml` | Path to the [dataset](https://docs.ultralytics.com/datasets) configuration file (default: `coco8.yaml`), essential for quantization. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md). For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLO11 TFLite Models ## Deploying Exported YOLO11 TFLite Models

10
docs/en/integrations/torchscript.md
View file

@ -105,6 +105,16 @@ Before diving into the usage instructions, it's important to note that while all
yolo predict model=yolo11n.torchscript source='https://ultralytics.com/images/bus.jpg' yolo predict model=yolo11n.torchscript source='https://ultralytics.com/images/bus.jpg'
``` ```
### Export Arguments
| Argument | Type | Default | Description |
| ---------- | ---------------- | ------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `torchscript` | Target format for the exported model, defining compatibility with various deployment environments. |
| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
| `optimize` | `bool` | `False` | Applies optimization for mobile devices, potentially reducing model size and improving performance. |
| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md). For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLO11 TorchScript Models ## Deploying Exported YOLO11 TorchScript Models

2
docs/en/macros/export-args.md
View file

@ -13,4 +13,4 @@
| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS) to the CoreML export, essential for accurate and efficient detection post-processing. | | `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS) to the CoreML export, essential for accurate and efficient detection post-processing. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. | | `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
| `device` | `str` | `None` | Specifies the device for exporting: GPU (`device=0`), CPU (`device=cpu`), MPS for Apple silicon (`device=mps`) or DLA for NVIDIA Jetson (`device=dla:0` or `device=dla:1`). | | `device` | `str` | `None` | Specifies the device for exporting: GPU (`device=0`), CPU (`device=cpu`), MPS for Apple silicon (`device=mps`) or DLA for NVIDIA Jetson (`device=dla:0` or `device=dla:1`). |
| `data` | `str` | `coco8.yaml` | Path to the dataset configuration file (default: `coco8.yaml`), essential for quantization. | | `data` | `str` | `coco8.yaml` | Path to the [dataset](https://docs.ultralytics.com/datasets) configuration file (default: `coco8.yaml`), essential for quantization. |