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Discover the cutting-edge world of DETR Breakdown Part 3: Architecture and Details and see how it can revolutionize your projects!

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Object Detection gets a makeover! Faster R-CNNs are running. They have lost the battle, and Transformers have taken their place. Like most computer vision (and NLP) tasks, Transformers is the new breed of models set to dominate the world.

The Big Picture

Our latest blog post unravels the model and architecture of the DEtection TRansformers (DETR), an innovative framework that is reshaping the landscape of object detection. 

How It Works

Starting with a holistic overview, we delve into the Convolutional Neural Network (CNN) backbone that transforms raw images into discernible features. We then walk you through the Transformer Encoder and Decoder preprocessing layers, explaining each component's unique role in the DETR structure. Finally, as we approach the culmination of the architecture, we highlight the Feed-Forward Neural (FFN) layers, which are responsible for making predictions.

Our Thoughts

We believe DETR is a game-changer. Its unique use of Transformer block and set prediction loss is truly pioneering. By altering our approach to object detection, DETR has opened up a world of new possibilities.

Yes, But

Like any new architecture, DETR is not simple to grasp in the first take. However, with the right explanations and illustrative diagrams, the approach should make sense.

Stay Smart

Instead of scratching your head while reading the paper and struggling with math equations, read the blog post, review the methodologies and architecture, and get a full overview of the entire paper.

Click here to read the full tutorial

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