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Standardize `str` formatting in docs (#19276)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Lakshantha Dissanayake 4 weeks ago committed by GitHub
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  1. 12
      docs/en/guides/analytics.md
  2. 16
      docs/en/integrations/coreml.md
  3. 8
      docs/en/integrations/edge-tpu.md
  4. 2
      docs/en/integrations/mnn.md
  5. 12
      docs/en/integrations/ncnn.md
  6. 20
      docs/en/integrations/onnx.md
  7. 20
      docs/en/integrations/openvino.md
  8. 10
      docs/en/integrations/paddlepaddle.md
  9. 12
      docs/en/integrations/rockchip-rknn.md
  10. 12
      docs/en/integrations/sony-imx500.md
  11. 24
      docs/en/integrations/tensorrt.md
  12. 2
      docs/en/integrations/tf-graphdef.md
  13. 16
      docs/en/integrations/tf-savedmodel.md
  14. 16
      docs/en/integrations/tfjs.md
  15. 18
      docs/en/integrations/tflite.md
  16. 14
      docs/en/integrations/torchscript.md
  17. 40
      docs/en/macros/augmentation-args.md
  18. 2
      docs/en/macros/export-args.md
  19. 6
      docs/en/macros/sam-auto-annotate.md
  20. 2
      docs/en/macros/solutions-args.md
  21. 18
      docs/en/macros/track-args.md
  22. 2
      docs/en/macros/validation-args.md

12
docs/en/guides/analytics.md
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@ -100,12 +100,12 @@ This guide provides a comprehensive overview of three fundamental types of [data
Here's a table with the `Analytics` arguments:
| Name | Type | Default | Description |
| ---------------- | ------ | ------- | ---------------------------------------------------- |
| `analytics_type` | `str` | `line` | Type of graph i.e "line", "bar", "area", "pie" |
| `model` | `str` | `None` | Path to Ultralytics YOLO Model File |
| `line_width` | `int` | `2` | Line thickness for bounding boxes. |
| `show` | `bool` | `False` | Flag to control whether to display the video stream. |
| Name | Type | Default | Description |
| ---------------- | ------ | -------- | ---------------------------------------------------- |
| `analytics_type` | `str` | `'line'` | Type of graph i.e "line", "bar", "area", "pie" |
| `model` | `str` | `None` | Path to Ultralytics YOLO Model File |
| `line_width` | `int` | `2` | Line thickness for bounding boxes. |
| `show` | `bool` | `False` | Flag to control whether to display the video stream. |
### Arguments `model.track`

16
docs/en/integrations/coreml.md
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@ -107,14 +107,14 @@ Before diving into the usage instructions, be sure to check out the range of [YO
### 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. |
| 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).

8
docs/en/integrations/edge-tpu.md
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@ -97,10 +97,10 @@ Before diving into the usage instructions, it's important to note that while all
### 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. |
| 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).

2
docs/en/integrations/mnn.md
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@ -70,7 +70,7 @@ Before diving into the usage instructions, it's important to note that while all
| Argument | Type | Default | Description |
| -------- | ---------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `mnn` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |

12
docs/en/integrations/ncnn.md
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@ -99,12 +99,12 @@ Before diving into the usage instructions, it's important to note that while all
### 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. |
| 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).

20
docs/en/integrations/onnx.md
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@ -115,16 +115,16 @@ Before diving into the usage instructions, be sure to check out the range of [YO
### 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. |
| 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).

20
docs/en/integrations/openvino.md
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@ -59,16 +59,16 @@ Export a YOLOv8n model to OpenVINO format and run inference with the exported mo
## Export Arguments
| Argument | Type | Default | Description |
| --------- | ---------------- | ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `openvino` | 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. |
| `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. |
| Argument | Type | Default | Description |
| --------- | ---------------- | -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `'openvino'` | 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. |
| `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).

10
docs/en/integrations/paddlepaddle.md
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@ -112,11 +112,11 @@ Before diving into the usage instructions, it's important to note that while all
### 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. |
| 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).

12
docs/en/integrations/rockchip-rknn.md
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@ -93,12 +93,12 @@ For detailed instructions and best practices related to the installation process
### 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) |
| 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).

12
docs/en/integrations/sony-imx500.md
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@ -87,12 +87,12 @@ yolov8n_imx_model
## Export 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. |
| 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. |
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).

24
docs/en/integrations/tensorrt.md
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@ -109,18 +109,18 @@ Before diving into the usage instructions, be sure to check out the range of [YO
### 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. |
| 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).

2
docs/en/integrations/tf-graphdef.md
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@ -107,7 +107,7 @@ Before diving into the usage instructions, it's important to note that while all
| Argument | Type | Default | Description |
| -------- | ---------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `format` | `str` | `pb` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |

16
docs/en/integrations/tf-savedmodel.md
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@ -99,14 +99,14 @@ Before diving into the usage instructions, it's important to note that while all
### 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. |
| 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).

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docs/en/integrations/tfjs.md
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@ -97,14 +97,14 @@ Before diving into the usage instructions, it's important to note that while all
### 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. |
| 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).

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@ -103,15 +103,15 @@ Before diving into the usage instructions, it's important to note that while all
### 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. |
| 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).

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### 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. |
| 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).

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| Argument | Type | Default | Range | Description |
| ----------------- | ------- | ------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `hsv_h` | `float` | `0.015` | `0.0 - 1.0` | Adjusts the hue of the image by a fraction of the color wheel, introducing color variability. Helps the model generalize across different lighting conditions. |
| `hsv_s` | `float` | `0.7` | `0.0 - 1.0` | Alters the saturation of the image by a fraction, affecting the intensity of colors. Useful for simulating different environmental conditions. |
| `hsv_v` | `float` | `0.4` | `0.0 - 1.0` | Modifies the value (brightness) of the image by a fraction, helping the model to perform well under various lighting conditions. |
| `degrees` | `float` | `0.0` | `-180 - +180` | Rotates the image randomly within the specified degree range, improving the model's ability to recognize objects at various orientations. |
| `translate` | `float` | `0.1` | `0.0 - 1.0` | Translates the image horizontally and vertically by a fraction of the image size, aiding in learning to detect partially visible objects. |
| `scale` | `float` | `0.5` | `>=0.0` | Scales the image by a gain factor, simulating objects at different distances from the camera. |
| `shear` | `float` | `0.0` | `-180 - +180` | Shears the image by a specified degree, mimicking the effect of objects being viewed from different angles. |
| `perspective` | `float` | `0.0` | `0.0 - 0.001` | Applies a random perspective transformation to the image, enhancing the model's ability to understand objects in 3D space. |
| `flipud` | `float` | `0.0` | `0.0 - 1.0` | Flips the image upside down with the specified probability, increasing the data variability without affecting the object's characteristics. |
| `fliplr` | `float` | `0.5` | `0.0 - 1.0` | Flips the image left to right with the specified probability, useful for learning symmetrical objects and increasing dataset diversity. |
| `bgr` | `float` | `0.0` | `0.0 - 1.0` | Flips the image channels from RGB to BGR with the specified probability, useful for increasing robustness to incorrect channel ordering. |
| `mosaic` | `float` | `1.0` | `0.0 - 1.0` | Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding. |
| `mixup` | `float` | `0.0` | `0.0 - 1.0` | Blends two images and their labels, creating a composite image. Enhances the model's ability to generalize by introducing label noise and visual variability. |
| `copy_paste` | `float` | `0.0` | `0.0 - 1.0` | Copies and pastes objects across images, useful for increasing object instances and learning object occlusion. Requires segmentation labels. |
| `copy_paste_mode` | `str` | `flip` | - | Copy-Paste augmentation method selection among the options of (`"flip"`, `"mixup"`). |
| `auto_augment` | `str` | `randaugment` | - | Automatically applies a predefined augmentation policy (`randaugment`, `autoaugment`, `augmix`), optimizing for classification tasks by diversifying the visual features. |
| `erasing` | `float` | `0.4` | `0.0 - 0.9` | Randomly erases a portion of the image during classification training, encouraging the model to focus on less obvious features for recognition. |
| `crop_fraction` | `float` | `1.0` | `0.1 - 1.0` | Crops the classification image to a fraction of its size to emphasize central features and adapt to object scales, reducing background distractions. |
| Argument | Type | Default | Range | Description |
| ----------------- | ------- | --------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `hsv_h` | `float` | `0.015` | `0.0 - 1.0` | Adjusts the hue of the image by a fraction of the color wheel, introducing color variability. Helps the model generalize across different lighting conditions. |
| `hsv_s` | `float` | `0.7` | `0.0 - 1.0` | Alters the saturation of the image by a fraction, affecting the intensity of colors. Useful for simulating different environmental conditions. |
| `hsv_v` | `float` | `0.4` | `0.0 - 1.0` | Modifies the value (brightness) of the image by a fraction, helping the model to perform well under various lighting conditions. |
| `degrees` | `float` | `0.0` | `-180 - +180` | Rotates the image randomly within the specified degree range, improving the model's ability to recognize objects at various orientations. |
| `translate` | `float` | `0.1` | `0.0 - 1.0` | Translates the image horizontally and vertically by a fraction of the image size, aiding in learning to detect partially visible objects. |
| `scale` | `float` | `0.5` | `>=0.0` | Scales the image by a gain factor, simulating objects at different distances from the camera. |
| `shear` | `float` | `0.0` | `-180 - +180` | Shears the image by a specified degree, mimicking the effect of objects being viewed from different angles. |
| `perspective` | `float` | `0.0` | `0.0 - 0.001` | Applies a random perspective transformation to the image, enhancing the model's ability to understand objects in 3D space. |
| `flipud` | `float` | `0.0` | `0.0 - 1.0` | Flips the image upside down with the specified probability, increasing the data variability without affecting the object's characteristics. |
| `fliplr` | `float` | `0.5` | `0.0 - 1.0` | Flips the image left to right with the specified probability, useful for learning symmetrical objects and increasing dataset diversity. |
| `bgr` | `float` | `0.0` | `0.0 - 1.0` | Flips the image channels from RGB to BGR with the specified probability, useful for increasing robustness to incorrect channel ordering. |
| `mosaic` | `float` | `1.0` | `0.0 - 1.0` | Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding. |
| `mixup` | `float` | `0.0` | `0.0 - 1.0` | Blends two images and their labels, creating a composite image. Enhances the model's ability to generalize by introducing label noise and visual variability. |
| `copy_paste` | `float` | `0.0` | `0.0 - 1.0` | Copies and pastes objects across images, useful for increasing object instances and learning object occlusion. Requires segmentation labels. |
| `copy_paste_mode` | `str` | `'flip'` | - | Copy-Paste augmentation method selection among the options of (`"flip"`, `"mixup"`). |
| `auto_augment` | `str` | `'randaugment'` | - | Automatically applies a predefined augmentation policy (`randaugment`, `autoaugment`, `augmix`), optimizing for classification tasks by diversifying the visual features. |
| `erasing` | `float` | `0.4` | `0.0 - 0.9` | Randomly erases a portion of the image during classification training, encouraging the model to focus on less obvious features for recognition. |
| `crop_fraction` | `float` | `1.0` | `0.1 - 1.0` | Crops the classification image to a fraction of its size to emphasize central features and adapt to object scales, reducing background distractions. |

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| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS) to the exported model when supported (see Export Formats), improving detection post-processing efficiency. |
| `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](https://docs.ultralytics.com/datasets) 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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| Argument | Type | Default | Description |
| ------------ | ----------- | -------------- | ---------------------------------------------------------------------------------------------- |
| `data` | `str` | required | Path to directory containing target images for annotation or segmentation. |
| `det_model` | `str` | `"yolo11x.pt"` | YOLO detection model path for initial object detection. |
| `sam_model` | `str` | `"sam2_b.pt"` | SAM2 model path for segmentation (supports t/s/b/l variants and SAM2.1) and mobile_sam models. |
| `device` | `str` | `""` | Computation device (e.g., 'cuda:0', 'cpu', or '' for automatic device detection). |
| `det_model` | `str` | `'yolo11x.pt'` | YOLO detection model path for initial object detection. |
| `sam_model` | `str` | `'sam2_b.pt'` | SAM2 model path for segmentation (supports t/s/b/l variants and SAM2.1) and mobile_sam models. |
| `device` | `str` | `''` | Computation device (e.g., 'cuda:0', 'cpu', or '' for automatic device detection). |
| `conf` | `float` | `0.25` | YOLO detection confidence threshold for filtering weak detections. |
| `iou` | `float` | `0.45` | IoU threshold for Non-Maximum Suppression to filter overlapping boxes. |
| `imgsz` | `int` | `640` | Input size for resizing images (must be multiple of 32). |

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| `up_angle` | `float` | `145.0` | Angle threshold for detecting the "up" position in workouts monitoring. Can be adjusted based on the position of keypoints for different exercises. |
| `down_angle` | `float` | `90.0` | Angle threshold for detecting the "down" position in workouts monitoring. Adjust this based on keypoint positions for specific exercises. |
| `kpts` | `list` | `[6, 8, 10]` | List of keypoints used for monitoring workouts. These keypoints correspond to body joints or parts, such as shoulders, elbows, and wrists, for exercises like push-ups, pull-ups, squats, ab-workouts. |
| `analytics_type` | `str` | `line` | Specifies the type of analytics visualization to generate. Options include `"line"`, `"pie"`, `"bar"`, or `"area"`. The default is `"line"` for trend visualization. |
| `analytics_type` | `str` | `'line'` | Specifies the type of analytics visualization to generate. Options include `"line"`, `"pie"`, `"bar"`, or `"area"`. The default is `"line"` for trend visualization. |
| `json_file` | `str` | `None` | Path to the JSON file defining regions for parking systems or similar applications. Enables flexible configuration of analysis areas. |
| `records` | `int` | `5` | Total detections count that triggers an automated email notification about unusual activity. |

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| Argument | Type | Default | Description |
| --------- | ------- | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `source` | `str` | `None` | Specifies the source directory for images or videos. Supports file paths and URLs. |
| `persist` | `bool` | `False` | Enables persistent tracking of objects between frames, maintaining IDs across video sequences. |
| `tracker` | `str` | `botsort.yaml` | Specifies the tracking algorithm to use, e.g., `bytetrack.yaml` or `botsort.yaml`. |
| `conf` | `float` | `0.3` | Sets the confidence threshold for detections; lower values allow more objects to be tracked but may include false positives. |
| `iou` | `float` | `0.5` | Sets the [Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (IoU) threshold for filtering overlapping detections. |
| `classes` | `list` | `None` | Filters results by class index. For example, `classes=[0, 2, 3]` only tracks the specified classes. |
| `verbose` | `bool` | `True` | Controls the display of tracking results, providing a visual output of tracked objects. |
| Argument | Type | Default | Description |
| --------- | ------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `source` | `str` | `None` | Specifies the source directory for images or videos. Supports file paths and URLs. |
| `persist` | `bool` | `False` | Enables persistent tracking of objects between frames, maintaining IDs across video sequences. |
| `tracker` | `str` | `'botsort.yaml'` | Specifies the tracking algorithm to use, e.g., `bytetrack.yaml` or `botsort.yaml`. |
| `conf` | `float` | `0.3` | Sets the confidence threshold for detections; lower values allow more objects to be tracked but may include false positives. |
| `iou` | `float` | `0.5` | Sets the [Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (IoU) threshold for filtering overlapping detections. |
| `classes` | `list` | `None` | Filters results by class index. For example, `classes=[0, 2, 3]` only tracks the specified classes. |
| `verbose` | `bool` | `True` | Controls the display of tracking results, providing a visual output of tracked objects. |

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| `dnn` | `bool` | `False` | If `True`, uses the [OpenCV](https://www.ultralytics.com/glossary/opencv) DNN module for ONNX model inference, offering an alternative to [PyTorch](https://www.ultralytics.com/glossary/pytorch) inference methods. |
| `plots` | `bool` | `False` | When set to `True`, generates and saves plots of predictions versus ground truth for visual evaluation of the model's performance. |
| `rect` | `bool` | `True` | If `True`, uses rectangular inference for batching, reducing padding and potentially increasing speed and efficiency. |
| `split` | `str` | `val` | Determines the dataset split to use for validation (`val`, `test`, or `train`). Allows flexibility in choosing the data segment for performance evaluation. |
| `split` | `str` | `'val'` | Determines the dataset split to use for validation (`val`, `test`, or `train`). Allows flexibility in choosing the data segment for performance evaluation. |
| `project` | `str` | `None` | Name of the project directory where validation outputs are saved. |
| `name` | `str` | `None` | Name of the validation run. Used for creating a subdirectory within the project folder, where validation logs and outputs are stored. |

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