Transformer Vision (ViT)
Le Transformer Vision (ViT) est un type de modèle d'apprentissage profond that applies the transformer architecture, originally designed for traitement du langage naturel, to the field of computer vision. Unlike traditional réseaux de neurones convolutifs (CNNs), which rely on convolutions to extract features from images, ViT utilizes self-attention mechanisms to process image data.
In a Vision Transformer, an image is first divided into fixed-size patches, which are then flattened and linearly embedded into vectors. Each of these vectors is treated similarly to a word embedding in natural language processing. The model then applies the transformer architecture, which includes layers of multi-head self-attention and feed-forward neural networks, to learn relationships between different patches of the image.
This approach allows the model to capture long-range dependencies and contextual information more effectively than traditional methods. The self-attention mechanism enables the model to weigh the importance of different patches relative to each other, leading to improved performance in tasks such as classification d'image, object detection, and segmentation.
Les Transformers Vision ont montré des performances remarquables sur diverses bases de données de référence, often surpassing state-of-the-art CNNs. Their ability to scale with larger datasets and compute resources also makes them increasingly popular in the field of AI research. However, training ViTs typically requires significantly more data than CNNs to achieve optimal results, which can be a limitation in scenarios with limited labeled data.
Dans l'ensemble, le Transformer Vision représente un changement significatif dans la conception des modèles pour comprendre l'information visuelle, ouvrant de nouvelles voies pour les avancées dans les applications de vision par ordinateur.