Edge Impulse41,199 followers3h FollowKeeping track of inventory...

Edge Impulse

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Keeping track of inventory can be daunting, but the latest advancements allows businesses to now leverage AI to streamline the process and boost efficiency. Visit our new blog post to dive into how one sample edge AI-powered solution, using Raspberry Pi and BrainChip tools, can transform inventory tracking — reducing errors, saving time, and improving the bottom line.

“After setting a few model hyperparameters, Mendez initiated the training process with the click of a button. When complete, a confusion matrix was displayed to help assess the performance of the model. It was quite easy to interpret the results in this case — an F1 score of 100 percent had been achieved. That means all objects were detected, and there were no false positives.

Mendez has provided a GitHub repository where source code for this project can be downloaded. Once that code is paired with a MetaTF format file downloaded from Edge Impulse that contains the trained model, local inferences can be run on the Raspberry Pi equipped with an Akida PCIe Board. Mendez has written multiple implementations of the software, some with additional optimizations or larger bounding boxes drawn in the display, and some that use a USB webcam instead of the Camera Module 3. There is really no limit to the possible customizations, so the same pipeline could run on a different platform if desired.

With the BrainChip Akida Neuromorphic Hardware Accelerator, it was found that the system could run inferences at a whopping 56 frames per second! Not bad at all for a computer vision pipeline running on edge computing hardware. Moreover, this required less than 100 mW of power consumption.

https://www.linkedin.com/posts/edgeimpulse_shelf-awareness-tracking-inventory-with-activity-7231450866363285505-Sikn?utm_source=share&utm_medium=member_ios

Every business has a need for efficient low cost stock control.

My opinion only DYOR

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