1. GIT - Hugging Face
GIT is a decoder-only Transformer that leverages CLIP's vision encoder to condition the model on vision inputs besides text. The model obtains state-of-the-art ...
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2. Installation - Hugging Face
git clone https://github.com/huggingface/transformers.git cd transformers pip install -e . These commands will link the folder you cloned the repository to ...
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
3. [2403.09394] GiT: Towards Generalist Vision Transformer through ... - arXiv
Mar 14, 2024 · Abstract:This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable for various vision tasks only with a ...
This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable for various vision tasks only with a vanilla ViT. Motivated by the universality of the Multi-layer Transformer architecture (e.g, GPT) widely used in large language models (LLMs), we seek to broaden its scope to serve as a powerful vision foundation model (VFM). However, unlike language modeling, visual tasks typically require specific modules, such as bounding box heads for detection and pixel decoders for segmentation, greatly hindering the application of powerful multi-layer transformers in the vision domain. To solve this, we design a universal language interface that empowers the successful auto-regressive decoding to adeptly unify various visual tasks, from image-level understanding (e.g., captioning), over sparse perception (e.g., detection), to dense prediction (e.g., segmentation). Based on the above designs, the entire model is composed solely of a ViT, without any specific additions, offering a remarkable architectural simplification. GiT is a multi-task visual model, jointly trained across five representative benchmarks without task-specific fine-tuning. Interestingly, our GiT builds a new benchmark in generalist performance, and fosters mutual enhancement across tasks, leading to significant improvements compared to isolated training. This reflects a similar impact observed in LLMs. Further enriching training with 27 datasets, GiT achieves strong zero-shot results over va...
![[2403.09394] GiT: Towards Generalist Vision Transformer through ... - arXiv](http://fakehost/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
4. GIT: A Generative Image-to-text Transformer for Vision and Language
May 27, 2022 · Abstract:In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video ...
In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering. While generative models provide a consistent network architecture between pre-training and fine-tuning, existing work typically contains complex structures (uni/multi-modal encoder/decoder) and depends on external modules such as object detectors/taggers and optical character recognition (OCR). In GIT, we simplify the architecture as one image encoder and one text decoder under a single language modeling task. We also scale up the pre-training data and the model size to boost the model performance. Without bells and whistles, our GIT establishes new state of the arts on 12 challenging benchmarks with a large margin. For instance, our model surpasses the human performance for the first time on TextCaps (138.2 vs. 125.5 in CIDEr). Furthermore, we present a new scheme of generation-based image classification and scene text recognition, achieving decent performance on standard benchmarks. Codes are released at \url{https://github.com/microsoft/GenerativeImage2Text}.
5. What Is a Transformer Model? | NVIDIA Blogs
Mar 25, 2022 · A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in ...
Transformer models apply an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other.
6. Transformers.js
State-of-the-art Machine Learning for the web. Run Transformers directly in your browser, with no need for a server!
State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!
7. Transformers: the Google scientists who pioneered an AI revolution
Jul 22, 2023 · Transformers: the Google scientists who pioneered an AI revolution ... Simply sign up to the Technology sector myFT Digest -- delivered directly ...
Their paper paved the way for the rise of large language models. But all have since left the Silicon Valley giant
8. gatsby-transformer-gitinfo
Add some git information on File fields from latest commit: date, author and email. Install. npm install --save gatsby-transformer-gitinfo. Note: You also need ...
gatsby-transformer-gitinfo Add some git information on fields from latest commit: date, author and email. Install Note: You also need to…
9. [PDF] Gas Insulated Transformer(GIT) - Mitsubishi Electric
Gas Insulated Transformer(GIT). IEC-60076 part 15 gas-filled power transformers enacted in 2008. Non-flammable and non-explosive. Non-Flammable and Non ...
10. SentenceTransformers Documentation — Sentence ...
SentenceTransformers Documentation; Edit on GitHub. Note. Sentence Transformers v3.0 just released, introducing a new training API for Sentence Transformer ...
Sentence Transformers
11. Transformer Models and BERT Model | Google Cloud Skills Boost
p>This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model.
<p>This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.</p><p>This course is estimated to take approximately 45 minutes to complete.</p>
12. BERT: Pre-training of Deep Bidirectional Transformers for ...
google uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. ... Transformers. Unlike recent language ...
Jacob Devlin
