(ViT) An Image Is Worth 16x16 Words | Paper Explained скачать в хорошем качестве

(ViT) An Image Is Worth 16x16 Words | Paper Explained

New video about Vision Transformer(ViT) on my channel. As more flexible architecture, Transformers completely overtook the NLP field, but because of the quadratic cost of attention mechanism its application to Computer Vision field remained limited. “An Image is Worth 16x16 Words” paper is the first successful application of Transformers that beat the previous state-of-the-art results in image classification task. So in this video, I will explain concepts like: the motivation behind transformers in Computer Vision how the ViT works results they achieved the effect of model and dataset size on the performance of ViT the future directions and limitations of this models In the next video I will show the PyTorch code with pre-trained weights, so stay tuned for that! ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Paper: https://arxiv.org/abs/2010.11929 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Medium article about Weight Standardisation and Group Normalization:   / overview-of-normalization-techniques-in-de...   ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Connect with me on: Linkedin -   / maciej-balawejder-rt8015   GitHub - https://github.com/maciejbalawejder Medium -   / maciejbalawejder   Buy Me a Coffee - [https://www.buymeacoffee.com/mbalawejder](https://www.buymeacoffee.com/mbalawejder) ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Timestamps: 0:00 Introduction 1:20 Transformer Pros/Cons 2:34 CNNs Pros/Cons 3:36 Related work 4:49 How ViT works? 6:24 Training process 7:48 Results 9:21 Insights from the results 12:20 Conclusions