System and method for training a machine learning model

A method for training a machine learning model configured to recognize elements of an object or an environment, the method comprising steps to:

• collect input data related to at least a portion of the object or the environment from an input sensor;
• execute a feature extractor to extract features for a plurality of data elements of the input data;
• execute a clustering model configured to cluster the plurality of data elements of the input data into a plurality of feature clusters based on similarities of the extracted features to each other;
• apply a clustering filter on the plurality of feature clusters to output a plurality of target clusters from among the plurality of feature clusters;
• label a plurality of target data elements of the plurality of target clusters with respective ground truth predetermined labels for each target cluster;
• generate a training dataset by:
• matching clusters of the machine learning model with the plurality of target clusters using an inference algorithm, running each target data element of the plurality of target clusters through the machine learning model in inference mode to generate prediction labels;
• and
• replacing the ground truth predetermined labels of the target clusters with the prediction labels generated by the machine learning model, so as to generate the training dataset comprising the plurality of target data elements of the plurality of target clusters labeled with the prediction labels generated by the machine learning model;
• and
• update a training of the machine learning model in a training mode using the training dataset by performing hyper-parameter fine-tuning on the machine learning model for the plurality of target clusters in the training dataset.

A computing system for training an object detection machine learning model configured to recognize objects in images of an object or an environment, the system comprising:

• a camera;
• a processor;
• and
• a memory storing executable instructions that, in response to execution by the processor cause the processor to:
• collect image data related to at least a portion of the object or the environment from the camera;
• perform object detection to crop images of the image data to generate a plurality of cropped images capturing detected objects;
• execute a feature extractor to extract features for the plurality of cropped images;
• execute a clustering model configured to cluster the plurality of cropped images of the image data into a plurality of feature clusters based on similarities of the extracted features to each other;
• apply a clustering filter on the plurality of feature clusters to output a plurality of target clusters from among the plurality of feature clusters;
• label a plurality of cropped target images of the plurality of target clusters with respective ground truth predetermined object labels for each target cluster;
• generate a training dataset by:
• matching clusters of the object detection machine learning model with the plurality of target clusters using an inference algorithm, running each cropped target image of the plurality of target clusters through the object detection machine learning model in inference mode to generate prediction labels;
• and
• replacing the ground truth predetermined object labels of the target clusters with the prediction labels generated by the object detection machine learning model, so as to generate the training dataset comprising the plurality of cropped target images of the plurality of target clusters labeled with the prediction labels generated by the object detection machine learning model;
• and
• update a training of the object detection machine learning model in a training mode using the training dataset by performing hyper-parameter fine-tuning on the object detection machine learning model for the plurality of target clusters in the training dataset.