Unlocking visual insights with Vision Transformers
We’ve explored the advantages, applications, and economic benefits of Vision Transformers in computer vision tasks, redefining image analysis for improved performance and efficiency.
#Computer Vision
#Deep Learning
#Self Attention
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What are Vision Transformers?
Vision Transformers (ViTs) are advanced deep learning architectures that are transforming computer vision tasks by delivering impressive performance, capturing global information effectively, and efficiently handling long-term dependencies, driving significant advancements in the field of image analysis.
The main technologies behind ViTs
The key idea behind Vision Transformers is to treat image data as a sequence of patches, or regions, and use attention mechanisms to capture the relationships between regions to make a prediction. Let’s focus on the two main technologies behind ViTs.
Self-attention mechanisms
Vision Transformers (ViTs) use the self-attention mechanism to effectively prioritise some information of the input over others. Self-attention can be computed in parallel, which allows this architecture to be scalable and train on large datasets. Images are divided into smaller parts by the transformer-based neural networks and processed through self-attention and feedforward layers.
Self-supervised pre-training on large datasets
Vision Transformers (ViTs) can utilize self-supervised pre-training on large, readily available datasets to acquire general data representations, enabling easy fine-tuning for new tasks and datasets. Pre-training on unlabeled datasets enhances ViTs' capabilities and avoid costly human-labeled data, as the models learn by predicting missing parts of input images based on contextual information.
Core areas of application
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ViTs in action: Reply’s testing
With the aim to validate Vision Transformers in real-world applications, we turned our attention to the DINO model. DINO, which stands for "self-distillation with NO label," is an advanced AI model for computer vision tasks introduced by Meta Al in 2021.
Through intensive development efforts, we successfully applied the DINO pre-trained ViT to automate tasks in various use cases. Specifically, we leveraged DINO to extract meaningful features and detect objects without being specifically trained for them. All the use cases encompassed integrating DINO into Spot, Boston Dynamics' most friendly and agile autonomous robot, to safely perform monitoring and inspection of industrial sites. As an example, after the integration, Spot could automatically read measurements from industrial processes and take data-driven actions accordingly, while being extremely data efficient. Reply has also evaluated VC-1, CLIP, SAM and Grounding DINO, all of which are driving breakthrough innovation in Computer Vision.
embark on a transformative journey in computer vision
Are you ready to unlock the potential of Vision Transformers? Connect with us to explore the latest applications and use cases.
