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Detect bounding boxes in images using specified model, segment objects, generate masks, customize sensitivity and refinement.
BboxDetectorSEGS is a powerful node designed to detect bounding boxes within images using a specified model. This node is particularly useful for AI artists who need to identify and segment objects within their artwork or images. By leveraging advanced detection algorithms, BboxDetectorSEGS can accurately identify objects based on the provided model and generate masks that highlight these objects. The node also allows for customization of detection sensitivity and mask refinement through threshold and dilation parameters, ensuring that you can fine-tune the results to meet your specific needs. This makes BboxDetectorSEGS an essential tool for enhancing image analysis and object detection tasks in creative projects.
This parameter specifies the bounding box detector model to be used for object detection. The model should be compatible with the node and capable of processing the input image to identify objects. The choice of model can significantly impact the accuracy and performance of the detection process.
The image parameter is the input image in which the bounding boxes are to be detected. This image should be in a format that the node can process, typically a tensor representation of the image. The quality and resolution of the image can affect the detection results.
The threshold parameter determines the confidence level required for an object to be considered detected. It is a floating-point value ranging from 0.0 to 1.0, with a default value of 0.5. A higher threshold means that only objects with higher confidence scores will be detected, reducing false positives but potentially missing some objects. Conversely, a lower threshold increases sensitivity but may result in more false positives.
The dilation parameter controls the amount of dilation applied to the detected masks. It is an integer value ranging from -512 to 512, with a default value of 4. Positive values increase the size of the detected masks, which can help in covering more area around the detected objects, while negative values reduce the mask size. Adjusting this parameter can help refine the mask to better fit the detected objects.
The mask output is a tensor representing the combined mask of all detected objects in the input image. This mask highlights the areas where objects have been detected, allowing for further processing or analysis. The mask is returned as a single-channel tensor, with each pixel value indicating the presence or absence of an object.
threshold
parameter to balance between detection sensitivity and accuracy. A higher threshold reduces false positives but may miss some objects, while a lower threshold increases sensitivity but may result in more false positives.dilation
parameter to refine the detected masks. Positive values can help cover more area around objects, while negative values can make the masks more precise.© Copyright 2024 RunComfy. All Rights Reserved.