Git Transformers (2024)

1. GIT - Hugging Face

  • GitVisionConfig · GitVisionModel · GitConfig · GitProcessor

  • 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. [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...

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. huggingworld / transformers - GitLab

  • Jun 30, 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. LLM Transformer Model Visually Explained

  • An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.

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. Installation — Sentence Transformers documentation

  • Installation¶ ; Install with pip¶ · pip · - ; Install with Conda¶ · conda · - ; Install from Source¶ · pip · git ...

  • We recommend Python 3.8+, PyTorch 1.11.0+, and transformers v4.34.0+. There are three options to install Sentence Transformers:

9. Transformers GitHub Release Updates | Restackio

  • To install Transformers directly from GitHub, you can use the following command: pip install git+https://github.com/huggingface/transformers. This command ...

  • Explore the latest updates and features in the Transformers GitHub release, enhancing your machine learning projects. | Restackio

10. Transformers.js

  • Transformers.js. Run Transformers in your ... Image classification w/ google/vit-base-patch16-224 (88 MB) ... transformers!') # [{'label': 'POSITIVE ...

  • State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!

11. GIT: A Generative Image-to-text Transformer for Vision and Language

  • May 27, 2022 · GIT: A Generative Image-to-text Transformer for Vision and Language ... In this paper, we design and train a Generative Image-to-text Transformer, ...

  • 🏆 SOTA for Image Captioning on nocaps-XD near-domain (CIDEr metric)

12. A Transformer-Based Deep Learning Model for Geoacoustic Inversion

  • May 24, 2023 · The present study presents a novel geoacoustic inversion model known as the geoacoustic inversion transformer (GIT). The transformer ...

  • Geoacoustic inversion is a challenging task in marine research due to the complex environment and acoustic propagation mechanisms. With the rapid development of deep learning, various designs of neural networks have been proposed to solve this issue with satisfactory results. As a data-driven method, deep learning networks aim to approximate the inverse function of acoustic propagation by extracting knowledge from multiple replicas, outperforming conventional inversion methods. However, existing deep learning networks, mainly incorporating stacked convolution and fully connected neural networks, are simple and may neglect some meaningful information. To extend the network backbone for geoacoustic inversion, this paper proposes a transformer-based geoacoustic inversion model with additional frequency and sensor 2-D positional embedding to perceive more information from the acoustic input. The simulation experimental results indicate that our proposed model achieves comparable inversion results with the existing inversion networks, demonstrating its effectiveness in marine research.

13. GiT: Graph Interactive Transformer for Vehicle Re-Identification

  • Jan 26, 2023 · In addition to the interaction between graphs and transforms, the graph is a newly-designed local correction graph, which learns discriminative ...

  • 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.

14. 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>

Git Transformers (2024)
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