Vision Transformer

This blog post will show how easy it is to fine-tune pre-trained Transformer models for your dataset using the Hugging Face Optimum library on Graphcore Intelligence Processing Units (IPUs). Written by: Tim Santos, Eva Woodbridge (Graphcore), Julien Simon (Hugging Face) This blog post will show how easy it is to…

It has been a while since the last review of mine, but two recent Vision Transformer architectures, GC ViT and Next-ViT, sparked my interest very much. I was so impressed by novelties and contributions to ViT that I made a thorough review and held a technical talk at Data Science…

A technical walkthrough on how to get up and running with Graphcore IPUs on Paperspace using our HuggingFace Optimum vision transformer example. Paperspace is an industry-leading MLOPs platform specialising in on-demand high-performance computing. Thanks to a new partnership with Graphcore, any Paperspace user can now quickly access Intelligent Processing Unit…

Using SageMaker distributed data parallel library to speedup image classification model training by up to 40% Vision Transformers (ViT) changes the Computer Vision(CV) paradigm by solving the dimension issue with splitting the image into patches of fixed sizes, which then are transformed to one-dimension embeddings or pass into a convolutional…

首先，我們先談談將 Transformer 引入 CV 的難處 ViT 並不是第一個嘗試將 Transformer 引入 CV 領域的論文，在 Transformer 霸榜 NLP 領域後，許多論文就開始嘗試將 Transformer 應用到 CV 領域，但發現了一個痛點，就是計算量非常驚人！ 在 NLP 領域使用 Transformer 時，一個長度為 N 的句子，會得到 N 個 key 和 N 個 quer …

In the previous article, we discussed in brief about encoder decoders and our approach towards solving the task of captioning. We fine-tuned a language model, this allowed the decoder to learn new words, generate brief captions and save training time. This can be referred as priming our decoder before actual…

Outperforms PVT/PVTv1, Swin Transformer, Twins PVTv2: Improved Baselines with Pyramid Vision Transformer PVTv2, by Nanjing University, The University of Hong Kong, Nanjing University of Science and Technology, IIAI, and SenseTime Research 2022 CVMJ, Over 90 Citations (Sik-Ho Tsang @ Medium) Image Classification, Vision Transformer, ViT, PVT/PVTv1, Swin Transformer Compared with…

論文重點 將自然語言領域提出的Transformer應用於電腦視覺領域，提出Vision Transformer。 使用大資料庫時模型表現比ResNet好，且訓練時長大幅縮減。 注意力機制讓模型從淺到深層都能利用整張影像的資訊，此外也觀察到類似CNN的特徵。 原始論文：An Image is Worth 16x16 Words: Transformers for …

Important Links TrOCR — Paper: TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models TrOCR — Models Library: https://huggingface.co/models?filter=trocr TrOCR — Most Downloaded Model: https://huggingface.co/microsoft/trocr-base-printed TrOCR — Hugging Face Home Page: https://huggingface.co/docs/transformers/model_doc/trocr TrOCR — Notebooks: https://github.com/NielsRogge/Transformers-Tutorials/tree/master/TrOCR