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 ...
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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. GIT: A Generative Image-to-text Transformer for Vision and Language
27 mei 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}.
4. [2403.09394] GiT: Towards Generalist Vision Transformer through ... - arXiv
14 mrt 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...
5. huggingworld / transformers - GitLab
30 jun 2020 · Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
6. GIT: A Generative Image-to-text Transformer for Vision and Language
27 mei 2022 · In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video ...
🏆 SOTA for Image Captioning on nocaps-XD near-domain (CIDEr metric)
7. [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 ...
8. Hugging Face Transformers Examples - Philschmid
26 jan 2023 · As we know, Transformers can be used to fine-tune models like BERT, but did you know that the GitHub repository of transformers provides over 20 ...
Learn how to leverage Hugging Face Transformers to easily fine-tune your models.
9. MeshGPT: Generating Triangle Meshes with Decoder-Only ...
MeshGPT creates triangle meshes by autoregressively sampling from a transformer model that has been trained to produce tokens from a learned geometric ...
MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers
10. GIT: A Generative Image-to-text Transformer for Vision and Language
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 ...
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...
11. GiT: Graph Interactive Transformer for Vehicle Re-Identification
26 jan 2023 · For that, a graph interactive transformer (GiT) is proposed in this paper. In the macro view, a list of GiT blocks are stacked to build a ...
Transformers are more and more popular in computer vision, which treat an image as a sequence of patches and learn robust global features from the sequence. However, pure transformers are not entirely suitable for vehicle re-identification because vehicle re-identification requires both robust global features and discriminative local features. For that, a graph interactive transformer (GiT) is proposed in this paper. In the macro view, a list of GiT blocks are stacked to build a vehicle re-identification model, in where graphs are to extract discriminative local features within patches and transformers are to extract robust global features among patches. In the micro view, graphs and transformers are in an interactive status, bringing effective cooperation between local and global features. Specifically, one current graph is embedded after the former level’s graph and transformer, while the current transform is embedded after the current graph and the former level’s transformer. In addition to the interaction between graphs and transforms, the graph is a newly-designed local correction graph, which learns discriminative local features within a patch by exploring nodes’ relationships. Extensive experiments on three large-scale vehicle re-identification datasets demonstrate that our GiT method is superior to state-of-the-art vehicle re-identification approaches.
12. The Illustrated Transformer - Jay Alammar
27 jun 2018 · Read the Attention Is All You Need paper, the Transformer blog post (Transformer ... Retrieved from https://jalammar.github.io/illustrated- ...
Discussions: Hacker News (65 points, 4 comments), Reddit r/MachineLearning (29 points, 3 comments) Translations: Arabic, Chinese (Simplified) 1, Chinese (Simplified) 2, French 1, French 2, Italian, Japanese, Korean, Persian, Russian, Spanish 1, Spanish 2, Vietnamese Watch: MIT’s Deep Learning State of the Art lecture referencing this post Featured in courses at Stanford, Harvard, MIT, Princeton, CMU and others In the previous post, we looked at Attention – a ubiquitous method in modern deep learning models. Attention is a concept that helped improve the performance of neural machine translation applications. In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained. The Transformer outperforms the Google Neural Machine Translation model in specific tasks. The biggest benefit, however, comes from how The Transformer lends itself to parallelization. It is in fact Google Cloud’s recommendation to use The Transformer as a reference model to use their Cloud TPU offering. So let’s try to break the model apart and look at how it functions. The Transformer was proposed in the paper Attention is All You Need. A TensorFlow implementation of it is available as a part of the Tensor2Tensor package. Harvard’s NLP group created a guide annotating the paper with PyTorch implementation. In this post, we will attempt to oversimplify things a bit and introduce the concepts one by one to hopefully make it easier to...
13. 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
14. Installation — Transformer Engine 1.6.0 documentation - NVIDIA Docs
Execute the following command to install the latest stable version of Transformer Engine: pip install git+https://github.com/NVIDIA/TransformerEngine.git@stable.
Linux x86_64
15. Install spaCy · spaCy Usage Documentation
... transformers] (with multiple comma-separated extras). ... macOS and OS X ship with Python and Git ... git clone https://github.com/explosion/spaCy cd spaCy make.
spaCy is a free open-source library for Natural Language Processing in Python. It features NER, POS tagging, dependency parsing, word vectors and more.
16. How to Incorporate Tabular Data with HuggingFace Transformers - Medium
23 okt 2020 · [Colab] [Github]. By Ken Gu. Transformer-based models are a game-changer when it comes to using unstructured text data. As of September 2020 ...
[Colab] [Github]
17. SF6 Gas-Insulated Power Transformer (GIT)01
To be an Excellent Solution Provider in Energy, Heavy Industrial and Power Systems in Singapore, and MENA Region. Read More. Contact Us. Oxley Biz Hub 2 62 Ubi ...
To be an Excellent Solution Provider in Energy, Heavy Industrial and Power Systems in Singapore, and MENA Region.
18. MaskGIT: Masked Generative Image Transformer | Papers With Code
2 best model for Text-to-Image Generation on LHQC (Block-FID metric)
#2 best model for Text-to-Image Generation on LHQC (Block-FID metric)
19. Transformer — PyTorch 2.3 documentation
Transformer. class torch.nn.Transformer(d_model=512, nhead=8, num_encoder_layers=6 ... Github Issues · Brand Guidelines. Stay up to date; Facebook · Twitter ...
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