| `format` | `str` | `coreml` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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. |
| `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. |
| `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. |
| `format` | `str` | `edgetpu` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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).
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
| `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. |
| `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. |
| `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. |
| `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. |
| `format` | `str` | `ncnn` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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. |
| `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. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
| `format` | `str` | `onnx` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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. |
| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
| `format` | `str` | `openvino` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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. |
| `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. |
| `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. |
| `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. |
| `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. |
| `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).
| `format` | `str` | `paddle` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
| `format` | `str` | `rknn` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
| `format` | `str` | `imx` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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. |
| `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. |
| `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).
| `format` | `str` | `engine` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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. |
| `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. |
| `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. |
@ -120,7 +120,7 @@ Before diving into the usage instructions, be sure to check out the range of [YO
| `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. |
| `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. |
| `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. |
| `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. |
| `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).
| `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. |
| `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. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
| `format` | `str` | `saved_model` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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. |
| `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. |
| `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. |
| `format` | `str` | `tfjs` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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. |
| `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. |
| `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. |
| `format` | `str` | `tflite` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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. |
| `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. |
| `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. |
| `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. |
| `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. |
| `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).
| `format` | `str` | `torchscript` | Target format for the exported model, defining compatibility with various deployment environments. |
| `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. |
| `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. |
| `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. |
| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS), essential for accurate and efficient detection post-processing. |
| `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_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_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. |
| `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. |
@ -14,7 +14,7 @@
| `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. |
| `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. |
| `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` | `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"`). |
| `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. |
| `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. |
| `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. |
| `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. |
| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS) to the exported model when supported (see Export Formats), improving detection post-processing efficiency. |
| `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. |
| `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](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. |
| `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. |
| `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. |
| `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. |
| `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. |
| `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. |
| `records` | `int` | `5` | Total detections count that triggers an automated email notification about unusual activity. |
| `source` | `str` | `None` | Specifies the source directory for images or videos. Supports file paths and URLs. |
| `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. |
| `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`. |
| `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. |
| `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. |
| `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. |
| `classes` | `list` | `None` | Filters results by class index. For example, `classes=[0, 2, 3]` only tracks the specified classes. |
| `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. |
| `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. |
| `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. |
| `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. |
| `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. |
| `name` | `str` | `None` | Name of the validation run. Used for creating a subdirectory within the project folder, where validation logs and outputs are stored. |
Lakshantha Dissanayake
1 month ago
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