随着人工智能技术的蓬勃发展，Transformer模型的影响力和应用范围也在不断扩大。这个改变游戏规则的模型已经成为学界与业界探索新技术的关键。面对这一趋势，迅速掌握并应用Transformer将极大增强个人的行业竞争力，为未来的人工智能发展打下坚实的基础。 返回搜狐，查看更多 ...

在本文中，我们将详细探讨目标检测，介绍视觉Transformer的强大功能，并通过一个实际项目逐步演示如何使用ViT进行目标检测。 目标检测是计算机视觉中的一项核心任务，推动了从自动驾驶汽车到实时视频监控等技术的发展。它涉及在图像中检测和定位物体 ...

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Pytorch reimplementation of Google's repository for the ViT model that was released with the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy, ...

Abstract: Recently Vision Transformer (ViT) and Convolution Neural Network (CNN) start to emerge as a hybrid deep architecture with better model capacity, generalization, and latency trade-off. Most ...

In 2017, a significant change reshaped Artificial Intelligence (AI). A paper titled Attention Is All You Need introduced ...

Recently, several methods have adopted the vision Transformer (ViT) in FGVC tasks since the data specificity of the multihead self-attention (MSA) mechanism in ViT is beneficial for extracting ...

使用PyTorch 从头开始实现 ViT模型代码，在 CIFAR-10 数据集上训练ViT模型 以完成图像分类。 ViT的架构 ViT 的架构受到 BERT 的启发，BERT 是一种仅编码器的 transformer 模型，通常用于文本分类或命名实体识别等 NLP 监督学习任务。ViT 背后的主要思想是，图像可以看作是 ...