Image File Inferencing

This chapter guides you through building your first image inferencing application using Edgematic.

GStreamer pipeline is the core framework for integrating models, data streams, and SiMa hardware. Let’s start with a ResNet50 classification pipeline example to learn how to configure components and establish a workflow for building EdgeMatic applications.

What is Resnet50?

ResNet-50 is a deep convolutional neural network renowned for its 50-layer architecture and the introduction of residual learning through skip connections. This design addresses challenges like vanishing gradients, enabling the training of significantly deeper networks. Since its introduction, ResNet-50 has become a foundational model in computer vision, excelling in tasks such as image classification, object detection, and segmentation. Its architecture has also influenced various deep learning frameworks and applications.

To faciliate with file operations, SiMa provides the simaaisrc plugin. This plugin operates similarly to the standard filesrc and multifilesrc plugins but optimized for SiMa MLA. It serves as a source element that reads data from files and feeds it into a GStreamer pipeline. This functionality is particularly useful for testing and debugging, as it allows developers to simulate various input scenarios by sourcing data from files, thereby facilitating isolated testing of individual components within a pipeline.

To get started, create a new Project named “ResNet50”:

Creating a new Project in Edgematic