Add Export Arguments tables to all Export docs (#18952)
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>
This commit is contained in:
Lakshantha Dissanayake
GitHub
parent
c0a4201370
commit
39b037408f
16 changed files with 162 additions and 33 deletions
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@ -105,6 +105,17 @@ Before diving into the usage instructions, be sure to check out the range of [YO
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yolo predict model=yolo11n.mlpackage source='https://ultralytics.com/images/bus.jpg'
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```
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### Export Arguments
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| Argument | Type | Default | Description |
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| -------- | ---------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `coreml` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
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| `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. |
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| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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## Deploying Exported YOLO11 CoreML Models
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@ -95,7 +95,14 @@ Before diving into the usage instructions, it's important to note that while all
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yolo predict model=yolo11n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
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```
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For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
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### Export Arguments
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| Argument | Type | Default | Description |
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| -------- | ---------------- | --------- | --------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `edgetpu` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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## Deploying Exported YOLO11 TFLite Edge TPU Models
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@ -66,7 +66,17 @@ Before diving into the usage instructions, it's important to note that while all
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yolo predict model='yolo11n.mnn' source='https://ultralytics.com/images/bus.jpg'
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```
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For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
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### Export Arguments
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| Argument | Type | Default | Description |
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| -------- | ---------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `mnn` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
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| `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. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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### MNN-Only Inference
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@ -97,7 +97,16 @@ Before diving into the usage instructions, it's important to note that while all
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yolo predict model='./yolo11n_ncnn_model' source='https://ultralytics.com/images/bus.jpg'
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```
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For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
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### Export Arguments
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| Argument | Type | Default | Description |
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| -------- | ---------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `ncnn` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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## Deploying Exported YOLO11 NCNN Models
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@ -113,6 +113,19 @@ Before diving into the usage instructions, be sure to check out the range of [YO
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yolo predict model=yolo11n.onnx source='https://ultralytics.com/images/bus.jpg'
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```
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### Export Arguments
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| Argument | Type | Default | Description |
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| ---------- | ---------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `onnx` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
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| `dynamic` | `bool` | `False` | Allows dynamic input sizes, enhancing flexibility in handling varying image dimensions. |
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| `simplify` | `bool` | `True` | Simplifies the model graph with `onnxslim`, potentially improving performance and compatibility. |
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| `opset` | `int` | `None` | Specifies the ONNX opset version for compatibility with different ONNX parsers and runtimes. If not set, uses the latest supported version. |
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| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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## Deploying Exported YOLO11 ONNX Models
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@ -57,21 +57,20 @@ Export a YOLOv8n model to OpenVINO format and run inference with the exported mo
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yolo predict model=yolov8n_openvino_model source='https://ultralytics.com/images/bus.jpg'
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```
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## Arguments
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## Export Arguments
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| Key | Value | Description |
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| --------- | ------------ | ------------------------------------------------------------------------------------------- |
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| `format` | `'openvino'` | format to export to |
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| `imgsz` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) |
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| `half` | `False` | FP16 quantization |
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| `int8` | `False` | INT8 quantization |
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| `batch` | `1` | [batch size](https://www.ultralytics.com/glossary/batch-size) for inference |
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| `dynamic` | `False` | allows dynamic input sizes |
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| `data` | `coco8.yaml` | Path to the dataset configuration file (default: `coco8.yaml`), essential for quantization. |
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| Argument | Type | Default | Description |
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| --------- | ---------------- | ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `openvino` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
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| `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. |
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| `dynamic` | `bool` | `False` | Allows dynamic input sizes, enhancing flexibility in handling varying image dimensions. |
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| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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| `data` | `str` | `coco8.yaml` | Path to the [dataset](https://docs.ultralytics.com/datasets) configuration file (default: `coco8.yaml`), essential for quantization. |
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!!! note
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When using `data` argument for quantization, please check [Dataset Guide](https://docs.ultralytics.com/datasets/detect) to learn more about the dataset format.
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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## Benefits of OpenVINO
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@ -110,7 +110,15 @@ Before diving into the usage instructions, it's important to note that while all
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yolo predict model='./yolo11n_paddle_model' source='https://ultralytics.com/images/bus.jpg'
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```
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For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
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### Export Arguments
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| Argument | Type | Default | Description |
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| -------- | ---------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `paddle` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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## Deploying Exported YOLO11 PaddlePaddle Models
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@ -91,6 +91,15 @@ For detailed instructions and best practices related to the installation process
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yolo export model=yolo11n.pt format=rknn name=rk3588 # creates '/yolo11n_rknn_model'
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```
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### Export Arguments
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| Argument | Type | Default | Description |
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| -------- | ---------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `rknn` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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| `name` | `str` | `rk3588` | Specifies the Rockchip model (rk3588, rk3576, rk3566, rk3568, rk3562, rv1103, rv1106, rv1103b, rv1106b, rk2118) |
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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## Deploying Exported YOLO11 RKNN Models
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@ -85,20 +85,16 @@ yolov8n_imx_model
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└── yolov8n_imx500_model.pbtxt
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```
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## Arguments
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## Export Arguments
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When exporting a model to IMX500 format, you can specify various arguments:
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| Argument | Type | Default | Description |
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| -------- | ---------------- | ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `imx` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `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. |
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| `data` | `str` | `coco8.yaml` | Path to the [dataset](https://docs.ultralytics.com/datasets) configuration file (default: `coco8.yaml`), essential for quantization. |
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| Key | Value | Description |
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| -------- | ------------ | -------------------------------------------------------------- |
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| `format` | `imx` | Format to export to (imx) |
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| `int8` | `True` | Enable INT8 quantization for the model (default: `True`) |
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| `imgsz` | `640` | Image size for the model input (default: `640`) |
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| `data` | `coco8.yaml` | Path to the dataset configuration file (default: `coco8.yaml`) |
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!!! note
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When using `data` argument for quantization, please check [Dataset Guide](https://docs.ultralytics.com/datasets/detect) to learn more about the dataset format.
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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## Using IMX500 Export in Deployment
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@ -107,6 +107,21 @@ Before diving into the usage instructions, be sure to check out the range of [YO
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yolo predict model=yolo11n.engine source='https://ultralytics.com/images/bus.jpg'
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```
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### Export Arguments
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| Argument | Type | Default | Description |
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| ----------- | ----------------- | ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `engine` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
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| `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. |
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| `dynamic` | `bool` | `False` | Allows dynamic input sizes, enhancing flexibility in handling varying image dimensions. |
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| `simplify` | `bool` | `True` | Simplifies the model graph with `onnxslim`, potentially improving performance and compatibility. |
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| `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. |
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| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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| `data` | `str` | `coco8.yaml` | Path to the [dataset](https://docs.ultralytics.com/datasets) configuration file (default: `coco8.yaml`), essential for quantization. |
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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### Exporting TensorRT with INT8 Quantization
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@ -103,7 +103,15 @@ Before diving into the usage instructions, it's important to note that while all
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yolo predict model='yolo11n.pb' source='https://ultralytics.com/images/bus.jpg'
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```
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For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
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### Export Arguments
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| Argument | Type | Default | Description |
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| -------- | ---------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `pb` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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## Deploying Exported YOLO11 TF GraphDef Models
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@ -97,7 +97,18 @@ Before diving into the usage instructions, it's important to note that while all
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yolo predict model='./yolo11n_saved_model' source='https://ultralytics.com/images/bus.jpg'
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```
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For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
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### Export Arguments
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| Argument | Type | Default | Description |
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| -------- | ---------------- | ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `saved_model` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `keras` | `bool` | `False` | Enables export to Keras format, providing compatibility with TensorFlow serving and APIs. |
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| `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. |
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| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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## Deploying Exported YOLO11 TF SavedModel Models
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@ -95,7 +95,18 @@ Before diving into the usage instructions, it's important to note that while all
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yolo predict model='./yolo11n_web_model' source='https://ultralytics.com/images/bus.jpg'
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```
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For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
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### Export Arguments
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| Argument | Type | Default | Description |
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| -------- | ---------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `tfjs` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
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| `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. |
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| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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## Deploying Exported YOLO11 TensorFlow.js Models
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@ -101,6 +101,18 @@ Before diving into the usage instructions, it's important to note that while all
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yolo predict model='yolo11n_float32.tflite' source='https://ultralytics.com/images/bus.jpg'
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```
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### Export Arguments
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| Argument | Type | Default | Description |
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| -------- | ---------------- | ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `tflite` | Target format for the exported model, defining compatibility with various deployment environments. |
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| `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. |
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| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
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| `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. |
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| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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| `data` | `str` | `coco8.yaml` | Path to the [dataset](https://docs.ultralytics.com/datasets) configuration file (default: `coco8.yaml`), essential for quantization. |
|
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|
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For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
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## Deploying Exported YOLO11 TFLite Models
|
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|
|
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|
@ -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'
|
||||
```
|
||||
|
||||
### Export Arguments
|
||||
|
||||
| Argument | Type | Default | Description |
|
||||
| ---------- | ---------------- | ------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `format` | `str` | `torchscript` | Target format for the exported model, defining compatibility with various deployment environments. |
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||||
| `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).
|
||||
|
||||
## Deploying Exported YOLO11 TorchScript Models
|
||||
|
|
|
|||
|
|
@ -13,4 +13,4 @@
|
|||
| `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. |
|
||||
| `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. |
|
||||
|
|
|
|||
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