Image Labeling
The ability to manage image annotations is a key to success in using AI-based visual inspection because it is difficult to organize large datasets and poor labeling leads to poor performance. PAQi offers a suite of data labeling software tools to manage the entire process of annotating images for training AI models regardless of the platform used for model training and without added costs. This is one of the differentiating features of PAQi that prevents you from being stuck in a single training platform or burdened with the high cost of cloud based annotation solutions.
Features
Label Manager
LM3 provides a label manager to manage all image annotations whether human or machine generated. With Label Manager a user can relabel objects across a complete dataset, search for objects by multiple classes using and, or, and not logic as well as add object labels by the advanced search criteria.
LabelIMG
LM3 coopted the open source package LabelImg to develop its own annotation software with advanced features.
Label Copying
With PAQi's label copying feature, users can quickly replicate labels from one image to another, saving time and effort during the labeling process. This feature is particularly useful for scenarios where multiple images contain similar objects or regions that require consistent labeling.
Image Augmentation
PAQi's Image Augmentation feature enables users to generate variations of labeled images by applying transformations such as rotation, scaling, flipping, and adding noise. This helps in augmenting the training dataset, increasing its diversity and robustness, and improving the generalization ability of AI models.
Object Cropping
For application involving ‘find and classify’, training the classify portion of this method is as easy as cropping objects by their object detector class. That feature coupled with the Label Manager functionality allow a user to quickly transition for a low accuracty object detection scheme to a high accuracy find and classify methodology.
Evaluation
PAQi provides built-in tools for evaluating the quality and accuracy of labeled datasets as well as the performance of trained detectors regardless of the platform they are trained on. The evaluation process helps in identifying and rectifying any discrepancies or errors in the labeling, leading to improved model performance.
Auto Generate Labels
PAQi's Auto Generate Labels feature automates the process of generating initial annotations for unlabeled images using pre-trained AI models or rule-based algorithms. This accelerates the labeling process by providing a starting point for manual refinement, saving time and resources while maintaining labeling accuracy.