SYSTEMS AND METHODS FOR LABELING DATA

1 . A method for labeling data, the method comprising the following steps:

A) providing, to an object-detection, machine-learning (ML) model, a plurality of unlabeled data such that the object-detection model predicts a plurality of regions;

B) correcting at least one vertex of bounds of at least one of the regions such that the bounds fit tighter around an object;

C) converting the regions to first subregions by cropping the first subregions from the unlabeled data; and

D) providing the first subregions to an embedding, ML model configured to output feature vectors for each of the first subregions.

2 . The method of claim 1 , further comprising:

cropping second subregions from labeled data; and

outputting, via the embedding model for each of the second subregions, feature vectors.

3 . The method of claim 2 , further comprising:

clustering the feature vectors of the first subregions into a plurality of clusters.

4 . The method of claim 3 , further comprising:

removing, via a user interface, the feature vectors of any cluster that do not resemble other objects in a same cluster.

5 . The method of claim 4 , further comprising:

automatically assigning a label to all of the feature vectors of the each first subregion in one of the clusters based on a similarity with the feature vectors of one of the second subregions; and

automatically assigning a different label to all of the feature vectors of the each first subregion in another one of the clusters based on a similarity with the feature vectors of another one of the second subregions.

6 . The method of claim 2 , further comprising:

augmenting the labeled data by storing the automatically-labeled subregions with the labeled data; and

repeating steps A-D using the augmented data and a new set of unlabeled data.

7 . The method of claim 2 , wherein the embedding model reduces dimensionality in the outputting of the feature vectors.

8 . The method of claim 6 , further comprising:

training both the object-detection model and the embedding model using the labeled data or the augmented data.

9 . A method for labeling data, the method comprising:

obtaining labeled data;

cropping second subregions from the labeled data; and

obtaining first subregions that are cropped from regions predicted by an object-detection model;

outputting, via an embedding model for each of the first and second subregions, feature vectors; and

clustering the feature vectors of the first subregions such that a label is determined for all subregions of each cluster, each of the determinations being based on the feature vectors of the second subregions.

10 . The method of claim 9 , wherein the embedding model is an ML model trained via supervised learning using the labeled data, and wherein the object-detection model is another different ML model trained via supervised learning using the labeled data.

11 . The method of claim 9 , further comprising:

removing, via a user interface, the feature vectors of any cluster that do not resemble other objects in a same cluster.

12 . The method of claim 9 , further comprising:

automatically assigning a label to all of the feature vectors of the each first subregion in one of the clusters based on a similarity with the feature vectors of one of the second subregions; and

automatically assigning a different label to all of the feature vectors of the each first subregion in another one of the clusters based on a similarity with the feature vectors of another one of the second subregions.

13 . The method of claim 12 , further comprising:

augmenting the labeled data by storing the automatically-labeled subregions with the labeled data.

14 . The method of claim 9 , wherein the embedding model reduces dimensionality in the outputting of the feature vectors.

15 . The method of claim 13 , further comprising:

training both the object-detection model and the embedding model using the augmented data.

16 . A system, comprising:

a first pipeline for creating first subregions from regions predicted in real-time; and

a second pipeline for creating second subregions from labeled regions and for labeling the first subregions, respectively in each of the clusters, using feature vectors generated from the second subregions.

17 . The system of claim 16 , wherein the first pipeline comprises a first ML model that is trained via supervised learning, and

wherein the second pipeline comprises a second ML model that is trained via triplet loss.

18 . The system of claim 17 , wherein the labeling is performed by clustering the first subregions into a plurality of clusters using feature vectors generated from the first subregions.

19 . The system of claim 18 , wherein all of the feature vectors are generated as part of the second pipeline.

20 . The system of claim 19 , wherein the first and second pipelines are reentered, and wherein the first and second ML models are retrained, using the labeled first subregions.