One of the big challenges with computer vision machine learning projects is performing training.
Why is it challenging?
Because historically it's been a very manual process thats time consuming and costly, but I've got some good news for you. You might be able to automate your training process with a new tool called Autodistill. Checkout this video to learn more.
Historically the cost of performing computer vision training has been expensive and often prohibitive but with Grounding Dino, you might be able to skip the training step. Watch this video to see how Grounding Dino, a Zero Shot Object Detector, performs object detection without custom training.
There are many interesting things you can do with computer vision, including detecting custom objects. In this video, we look at how to train a computer vision algorithm (YOLOv8) to detect custom items.
In this video, we demo computer vision at the edge using Mycelial to store data locally and send it back to the cloud. We’re using Mycelial to get a computer vision model to the edge where we are then doing inference and data storage locally and using Mycelial to move data back to the cloud (Snowflake in this case). This means that the computer vision inferencing will continue to run even if the network connection is lost. We can then trust that Mycelial will synchronize the data to the cloud when network connection is restored. This has a couple crucial advantages over sending all your data back to the cloud for processing, which we discuss in the video.