ComfyUI Node: MiVOLO Predictor (with Detector)

MiVOLO Predictor (with Detector):

The MiVOLOAgeGenderPredictorWithDetector is a sophisticated node designed to predict age and gender from images by leveraging advanced detection models. This node integrates a detection mechanism to identify and isolate relevant features such as faces and bodies within an image, which are then analyzed to provide age and gender predictions. The primary advantage of this node is its ability to handle complex images by focusing on specific areas of interest, thus enhancing the accuracy of predictions. It is particularly useful for applications where understanding demographic information from visual data is crucial, such as in digital art creation, targeted marketing, or user experience personalization. By combining detection and prediction capabilities, this node offers a comprehensive solution for extracting meaningful insights from images.

MiVOLO Predictor (with Detector) Input Parameters:

mivolo_model

The mivolo_model parameter specifies the model used for age and gender prediction. It is essential for processing the detected features and generating predictions. This parameter must be a valid MiVOLO model, which is pre-trained to understand and analyze human features.

detector_model

The detector_model parameter defines the model responsible for detecting faces and bodies within the input image. It is crucial for isolating the areas of interest that will be analyzed for age and gender prediction. This model should be a YOLO-based detector, which is known for its efficiency in object detection tasks.

image

The image parameter is the input image that the node will process. It serves as the source from which faces and bodies are detected and analyzed. The image should be in a format compatible with the detection and prediction models, typically a tensor or a standard image format.

mode

The mode parameter determines the detection strategy, offering options such as "Use persons and faces," "Use persons only," and "Use faces only." This setting influences which features are detected and subsequently analyzed, allowing for tailored predictions based on the specific needs of the task.

output_selection

The output_selection parameter allows you to choose between analyzing "All" detected features or focusing on the "Largest Person" in the image. This option helps refine the prediction process by concentrating on the most relevant or prominent features.

conf_threshold

The conf_threshold parameter sets the confidence threshold for detections, with a default value of 0.4. It ranges from 0.01 to 1.0 and determines the minimum confidence level required for a detection to be considered valid. Adjusting this threshold can help filter out less certain detections, improving the reliability of the results.

iou_threshold

The iou_threshold parameter specifies the Intersection over Union (IoU) threshold for non-max suppression, with a default value of 0.7. It ranges from 0.01 to 1.0 and is used to eliminate overlapping detections, ensuring that only the most relevant features are analyzed.

MiVOLO Predictor (with Detector) Output Parameters:

prediction_text

The prediction_text output provides a descriptive summary of the age and gender predictions, formatted as a human-readable string. This output is valuable for quickly understanding the results of the analysis without delving into numerical data.

age

The age output is a string representation of the predicted age for the detected features. It offers a straightforward way to access the age information derived from the image, which can be used for further analysis or decision-making processes.

gender

The gender output is a string indicating the predicted gender of the detected features. This output is crucial for applications that require demographic insights, enabling you to tailor content or experiences based on gender predictions.

MiVOLO Predictor (with Detector) Usage Tips:

• Ensure that the input image is clear and well-lit to improve detection accuracy and prediction reliability.
• Experiment with different mode settings to find the most effective detection strategy for your specific use case.
• Adjust the conf_threshold and iou_threshold parameters to fine-tune the balance between detection sensitivity and precision.

MiVOLO Predictor (with Detector) Common Errors and Solutions:

ValueError: MiVOLO Predictor (from Crops): At least one input (face_image or body_image) must be provided.

• Explanation: This error occurs when neither a face image nor a body image is provided for prediction.
• Solution: Ensure that at least one of the inputs, either a face image or a body image, is supplied to the node for processing.

RuntimeError: Failed to download detector '<model_name>'. Error: <error_message>

• Explanation: This error indicates that the specified detector model could not be downloaded from the Hugging Face Hub.
• Solution: Verify the model name and ensure that your internet connection is stable. If the problem persists, check for any network restrictions or try downloading the model manually.