Google ViT (Vision Transformer)

Google ViT (Vision Transformer) was developed by researchers at Google Research in 2020. It was created to explore the application of transformer models, which had shown success in natural language processing, for image recognition tasks. The goal was to leverage the self-attention mechanism of transformers to improve performance in computer vision applications.

Strengths of Google ViT include high accuracy in image classification and its ability to process large datasets efficiently using transformer architecture. Weaknesses involve high computational requirements and the need for substantial training data. Competitors include Convolutional Neural Networks (CNNs) like ResNet and EfficientNet, as well as other transformer-based models such as Facebook's DeiT (Data-efficient Image Transformer).

A Google ViT expert must have skills in deep learning, particularly with transformer models and image recognition. Proficiency in Python and frameworks like TensorFlow or PyTorch is essential. Knowledge of computer vision techniques, experience with large-scale datasets, and expertise in model optimization and fine-tuning are also crucial.