Library

The Library contains all of the images that are stored on the camera's SD card. These images are used for model training purposes, encompassing both successful and unsuccessful instances, and provide the means to teach the camera to recognize and differentiate between diverse objects, scenes, and environments. By exposing the camera to a wide range of images, it can learn to identify and classify objects with greater accuracy and efficiency.

Each image within the Library retains its associated metadata (date and time the image was captured, the camera settings used, and the GPS coordinates of the location where the image was taken). This metadata provides valuable information that can be used to understand the context of the image and can assist in refining the camera's recognition capabilities.

At the top of the page, you'll notice a few search bars that allow you to navigate through the library images. These search bars include the capture # and Trigger ID, which provide an easy way to locate specific images. Additionally, you can sort through the library images by the date and time they were captured. The library images are organized by different recipes, which store images for different types of inspections. By selecting a specific recipe, you can filter the images by inspection type to better organize and view your results. Once you've filtered the images by inspection type, you can further sort them by pass or fail, based on the inspection region and type. This feature allows you to easily identify any issues that may have occurred during the inspection process and helps to streamline the inspection process overall.

Once you have sorted through all the images, you will be able to view them on the screen with their respective thumbnails. Each image is labeled with the predicted outcome it is associated with, based on the image taken. This will help you quickly identify images that are relevant to your project. After identifying the relevant images, you can select multiple images at once by using the bulk selection feature. This will save you time and effort in selecting each image individually. Once you have selected the images, you can add them to the active recipe's trainset. This is particularly useful in machine learning, as it allows you to improve the accuracy of your model by retraining it with new data. In addition to adding images to the trainset, you can also download them in bulk by clicking on the download button. This will save you time if you need to use the images for any other purpose, such as creating a presentation or report. Overall, the ability to visually inspect and select images in bulk is a powerful feature that can greatly enhance your workflow and improve the accuracy of your models.