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. huggingface/transformers - Gitstar Ranking
Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. - View it on GitHub · https://huggingface.co/transformers. Star. 127462.
See the rank of huggingface/transformers on GitHub Ranking.
7. Fast Transformers for PyTorch
Transformers · Module fast_transformers · Attention · Builders
None
8. 7 Toshiba gas insulated transformers enter operation in Makkah
5 dagen geleden · Toshiba Energy Systems and Solutions Corporation has installed and activated seven gas insulated transformers (GIT) in the Haram 2 and Haram ...
Toshiba Energy Systems and Solutions Corporation has installed and activated seven gas insulated transformers (GIT) in the Haram 2 and Haram 3 substations serving Makkah, Saudi Arabia.
9. BERTopic - Maarten Grootendorst
BERTopic is a topic modeling technique that leverages transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst ...
Leveraging BERT and a class-based TF-IDF to create easily interpretable topics.
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. GIT: A Generative Image-to-text Transformer for Vision and Language
27 mei 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. [PDF] MaskGIT: Masked Generative Image Transformer - CVF Open Access
Example generation by MaskGIT on image synthesis and manipulation tasks. We show that MaskGIT is a flexible model that can generate high-quality samples on (a) ...
13. 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...
14. Installation — Transformer Engine 1.7.0 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
