600 Repositories
Python onnx-transformers Libraries
Global Tracking Transformers, CVPR 2022
Global Tracking Transformers Global Tracking Transformers, Xingyi Zhou, Tianwei Yin, Vladlen Koltun, Philipp Krähenbühl, CVPR 2022 (arXiv 2203.13250)
Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memories using approximate nearest neighbors, in Pytorch
Memorizing Transformers - Pytorch Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memori
ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers
ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers Official implementation of ViewFormer. ViewFormer is a NeRF-free neural rend
Lyrics generation with GPT2-based Transformer
HuggingArtists - Train a model to generate lyrics Create AI-Artist in just 5 minutes! 🚀 Run the demo notebook to train 🚀 Run the GUI demo to test Di
I will implement Fastai in each projects present in this repository.
DEEP LEARNING FOR CODERS WITH FASTAI AND PYTORCH The repository contains a list of the projects which I have worked on while reading the book Deep Lea
🐤 Nix-TTS: An Incredibly Lightweight End-to-End Text-to-Speech Model via Non End-to-End Distillation
🐤 Nix-TTS An Incredibly Lightweight End-to-End Text-to-Speech Model via Non End-to-End Distillation Rendi Chevi, Radityo Eko Prasojo, Alham Fikri Aji
Cross-view Transformers for real-time Map-view Semantic Segmentation (CVPR 2022 Oral)
Cross View Transformers This repository contains the source code and data for our paper: Cross-view Transformers for real-time Map-view Semantic Segme
Accelerated NLP pipelines for fast inference on CPU and GPU. Built with Transformers, Optimum and ONNX Runtime.
Optimum Transformers Accelerated NLP pipelines for fast inference 🚀 on CPU and GPU. Built with 🤗 Transformers, Optimum and ONNX runtime. Installatio
Implementation of the Transformer variant proposed in "Transformer Quality in Linear Time"
FLASH - Pytorch Implementation of the Transformer variant proposed in the paper Transformer Quality in Linear Time Install $ pip install FLASH-pytorch
Code release for "BoxeR: Box-Attention for 2D and 3D Transformers"
BoxeR By Duy-Kien Nguyen, Jihong Ju, Olaf Booij, Martin R. Oswald, Cees Snoek. This repository is an official implementation of the paper BoxeR: Box-A
Python package to generate image embeddings with CLIP without PyTorch/TensorFlow
imgbeddings A Python package to generate embedding vectors from images, using OpenAI's robust CLIP model via Hugging Face transformers. These image em
maximal update parametrization (µP)
Maximal Update Parametrization (μP) and Hyperparameter Transfer (μTransfer) Paper link | Blog link In Tensor Programs V: Tuning Large Neural Networks
Easy-to-use library to boost AI inference leveraging state-of-the-art optimization techniques.
NEW RELEASE How Nebullvm Works • Tutorials • Benchmarks • Installation • Get Started • Optimization Examples Discord | Website | LinkedIn | Twitter Ne
HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision
HugsVision is an open-source and easy to use all-in-one huggingface wrapper for computer vision. The goal is to create a fast, flexible and user-frien
🤗🖼️ HuggingPics: Fine-tune Vision Transformers for anything using images found on the web.
🤗 🖼️ HuggingPics Fine-tune Vision Transformers for anything using images found on the web. Check out the video below for a walkthrough of this proje
Curso práctico: NLP de cero a cien 🤗
Curso Práctico: NLP de cero a cien Comprende todos los conceptos y arquitecturas clave del estado del arte del NLP y aplícalos a casos prácticos utili
Building a real-time environment using webcam frame division in OpenCV and classify cropped images using a fine-tuned vision transformers on hybryd datasets samples for facial emotion recognition.
Visual Transformer for Facial Emotion Recognition (FER) This project has the aim to build an efficient Visual Transformer for the Facial Emotion Recog
Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch
CoCa - Pytorch Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch. They were able to elegantly fit in contras
HuggingSound: A toolkit for speech-related tasks based on HuggingFace's tools
HuggingSound HuggingSound: A toolkit for speech-related tasks based on HuggingFace's tools. I have no intention of building a very complex tool here.
Optical character recognition for Japanese text, with the main focus being Japanese manga
Manga OCR Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses a custom end-to-end model built with Tran
Implementation of ETSformer, state of the art time-series Transformer, in Pytorch
ETSformer - Pytorch Implementation of ETSformer, state of the art time-series Transformer, in Pytorch Install $ pip install etsformer-pytorch Usage im
This project shows how to serve an ONNX-optimized image classification model as a web service with FastAPI, Docker, and Kubernetes.
Deploying ML models with FastAPI, Docker, and Kubernetes By: Sayak Paul and Chansung Park This project shows how to serve an ONNX-optimized image clas
SentimentArcs: a large ensemble of dozens of sentiment analysis models to analyze emotion in text over time
SentimentArcs - Emotion in Text An end-to-end pipeline based on Jupyter notebooks to detect, extract, process and anlayze emotion over time in text. E
Implementation of CaiT models in TensorFlow and ImageNet-1k checkpoints. Includes code for inference and fine-tuning.
CaiT-TF (Going deeper with Image Transformers) This repository provides TensorFlow / Keras implementations of different CaiT [1] variants from Touvron
A Persian Image Captioning model based on Vision Encoder Decoder Models of the transformers🤗.
Persian-Image-Captioning We fine-tuning the Vision Encoder Decoder Model for the task of image captioning on the coco-flickr-farsi dataset. The implem
Official Code Implementation of the paper : XAI for Transformers: Better Explanations through Conservative Propagation
Official Code Implementation of The Paper : XAI for Transformers: Better Explanations through Conservative Propagation For the SST-2 and IMDB expermin
Contains the code and data for our #ICSE2022 paper titled as "CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences"
CodeFill This repository contains the code for our paper titled as "CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Namin
As-ViT: Auto-scaling Vision Transformers without Training
As-ViT: Auto-scaling Vision Transformers without Training [PDF] Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou In ICLR 2
QuadTree Attention for Vision Transformers (ICLR2022)
This repository contains codes for quadtree attention. This repo contains codes for feature matching, image classficiation, object detection and seman
(CVPR 2022) Pytorch implementation of "Self-supervised transformers for unsupervised object discovery using normalized cut"
(CVPR 2022) TokenCut Pytorch implementation of Tokencut: Self-supervised Transformers for Unsupervised Object Discovery using Normalized Cut Yangtao W
CLIP (Contrastive Language–Image Pre-training) for Italian
Italian CLIP CLIP (Radford et al., 2021) is a multimodal model that can learn to represent images and text jointly in the same space. In this project,
🏎️ Accelerate training and inference of 🤗 Transformers with easy to use hardware optimization tools
Hugging Face Optimum 🤗 Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to t
This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf
Behavior-Sequence-Transformer-Pytorch This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf This model
A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.
Basic-UI-for-GPT-J-6B-with-low-vram A repository to run GPT-J-6B on low vram systems by using both ram, vram and pinned memory. There seem to be some
Persian Bert For Long-Range Sequences
ParsBigBird: Persian Bert For Long-Range Sequences The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many ta
PyTorch implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Anomaly Transformer in PyTorch This is an implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. This pape
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology (LMRL Workshop, NeurIPS 2021)
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology Self-Supervised Vision Transformers Learn Visual Concepts in Histopatholog
Contextual Attention Network: Transformer Meets U-Net
Contextual Attention Network: Transformer Meets U-Net Contexual attention network for medical image segmentation with state of the art results on skin
[CVPR 2022] Official Pytorch code for OW-DETR: Open-world Detection Transformer
OW-DETR: Open-world Detection Transformer (CVPR 2022) [Paper] Akshita Gupta*, Sanath Narayan*, K J Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Sh
[CVPR 2022 Oral] TubeDETR: Spatio-Temporal Video Grounding with Transformers
TubeDETR: Spatio-Temporal Video Grounding with Transformers Website • STVG Demo • Paper This repository provides the code for our paper. This includes
REGTR: End-to-end Point Cloud Correspondences with Transformers
REGTR: End-to-end Point Cloud Correspondences with Transformers This repository contains the source code for REGTR. REGTR utilizes multiple transforme
End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model
onnx-facial-lmk-detector End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model, model.onnx. Demo You can
Code for "MetaMorph: Learning Universal Controllers with Transformers", Gupta et al, ICLR 2022
MetaMorph: Learning Universal Controllers with Transformers This is the code for the paper MetaMorph: Learning Universal Controllers with Transformers
Official Implementation of DE-DETR and DELA-DETR in "Towards Data-Efficient Detection Transformers"
DE-DETRs By Wen Wang, Jing Zhang, Yang Cao, Yongliang Shen, and Dacheng Tao This repository is an official implementation of DE-DETR and DELA-DETR in
[ICLR 2022] Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
AMOS This repository contains the scripts for fine-tuning AMOS pretrained models on GLUE and SQuAD 2.0 benchmarks. Paper: Pretraining Text Encoders wi
Official Implementation of DE-CondDETR and DELA-CondDETR in "Towards Data-Efficient Detection Transformers"
DE-DETRs By Wen Wang, Jing Zhang, Yang Cao, Yongliang Shen, and Dacheng Tao This repository is an official implementation of DE-CondDETR and DELA-Cond
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
beyond masking Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers The code is coming Figure 1: Pipeline of token-based pre-
[CVPR'22] Official PyTorch Implementation of Collaborative Transformers for Grounded Situation Recognition
[CVPR'22] Collaborative Transformers for Grounded Situation Recognition Paper | Model Checkpoint This is the official PyTorch implementation of Collab
Implementation of a protein autoregressive language model, but with autoregressive infilling objective (editing subsequences capability)
Protein GLM (wip) Implementation of a protein autoregressive language model, but with autoregressive infilling objective (editing subsequences capabil
Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.
scc4onnx Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel
Repository for fine-tuning Transformers 🤗 based seq2seq speech models in JAX/Flax.
Seq2Seq Speech in JAX A JAX/Flax repository for combining a pre-trained speech encoder model (e.g. Wav2Vec2, HuBERT, WavLM) with a pre-trained text de
Implementation of 🦩 Flamingo, state-of-the-art few-shot visual question answering attention net out of Deepmind, in Pytorch
🦩 Flamingo - Pytorch Implementation of Flamingo, state-of-the-art few-shot visual question answering attention net, in Pytorch. It will include the p
Direct application of DALLE-2 to video synthesis, using factored space-time Unet and Transformers
DALLE2 Video (wip) ** only to be built after DALLE2 image is done and replicated, and the importance of the prior network is validated ** Direct appli
Implementation of the GVP-Transformer, which was used in the paper "Learning inverse folding from millions of predicted structures" for de novo protein design alongside Alphafold2
GVP Transformer (wip) Implementation of the GVP-Transformer, which was used in the paper Learning inverse folding from millions of predicted structure
Code for the Findings of NAACL 2022(Long Paper): AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks
AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks arXiv link: upcoming To be published in Findings of NA
Python scripts performing class agnostic object localization using the Object Localization Network model in ONNX.
ONNX Object Localization Network Python scripts performing class agnostic object localization using the Object Localization Network model in ONNX. Ori
Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.
snc4onnx Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX. https://github.com/PINTO0309/simple-onnx-processing-tools 1.
PSTR: End-to-End One-Step Person Search With Transformers (CVPR2022)
PSTR (CVPR2022) This code is an official implementation of "PSTR: End-to-End One-Step Person Search With Transformers (CVPR2022)". End-to-end one-step
Implementation of MeMOT - Multi-Object Tracking with Memory - in Pytorch
MeMOT - Pytorch (wip) Implementation of MeMOT - Multi-Object Tracking with Memory - in Pytorch. This paper is just one in a line of work, but importan
A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for ONNX.
sam4onnx A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for
Simple ONNX operation generator. Simple Operation Generator for ONNX.
sog4onnx Simple ONNX operation generator. Simple Operation Generator for ONNX. https://github.com/PINTO0309/simple-onnx-processing-tools Key concept V
Simple node deletion tool for onnx.
snd4onnx Simple node deletion tool for onnx. I only test very miscellaneous and limited patterns as a hobby. There are probably a large number of bugs
A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.
sne4onnx A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or
Implementation of the Hybrid Perception Block and Dual-Pruned Self-Attention block from the ITTR paper for Image to Image Translation using Transformers
ITTR - Pytorch Implementation of the Hybrid Perception Block (HPB) and Dual-Pruned Self-Attention (DPSA) block from the ITTR paper for Image to Image
Under the hood working of transformers, fine-tuning GPT-3 models, DeBERTa, vision models, and the start of Metaverse, using a variety of NLP platforms: Hugging Face, OpenAI API, Trax, and AllenNLP
Transformers-for-NLP-2nd-Edition @copyright 2022, Packt Publishing, Denis Rothman Contact me for any question you have on LinkedIn Get the book on Ama
Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONNX.
ONNX-HybridNets-Multitask-Road-Detection Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONN
[CVPR 2022] "The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy" by Tianlong Chen, Zhenyu Zhang, Yu Cheng, Ahmed Awadallah, Zhangyang Wang
The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy Codes for this paper: [CVPR 2022] The Pr
Implementation of the state-of-the-art vision transformers with tensorflow
ViT Tensorflow This repository contains the tensorflow implementation of the state-of-the-art vision transformers (a category of computer vision model
Transformers-regression - Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates
Regression Free Model Update Code for the paper: Regression Bugs Are In Your Mod
Python code for ICLR 2022 spotlight paper EViT: Expediting Vision Transformers via Token Reorganizations
Expediting Vision Transformers via Token Reorganizations This repository contain
Hcaptcha-challenger - Gracefully face hCaptcha challenge with Yolov5(ONNX) embedded solution
hCaptcha Challenger 🚀 Gracefully face hCaptcha challenge with Yolov5(ONNX) embe
Yolox-bytetrack-sample - Python sample of MOT (Multiple Object Tracking) using YOLOX and ByteTrack
yolox-bytetrack-sample YOLOXとByteTrackを用いたMOT(Multiple Object Tracking)のPythonサン
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers Authors: Jaemin Cho, Abhay Zala, and Mohit Bansal (
ONNX-PackNet-SfM: Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX
Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX
TorchMD-Net provides state-of-the-art graph neural networks and equivariant transformer neural networks potentials for learning molecular potentials
TorchMD-net TorchMD-Net provides state-of-the-art graph neural networks and equivariant transformer neural networks potentials for learning molecular
OMLT: Optimization and Machine Learning Toolkit
OMLT is a Python package for representing machine learning models (neural networks and gradient-boosted trees) within the Pyomo optimization environment.
Convert BART models to ONNX with quantization. 3X reduction in size, and upto 3X boost in inference speed
fast-Bart Reduction of BART model size by 3X, and boost in inference speed up to 3X BART implementation of the fastT5 library (https://github.com/Ki6a
CATE: Computation-aware Neural Architecture Encoding with Transformers
CATE: Computation-aware Neural Architecture Encoding with Transformers Code for paper: CATE: Computation-aware Neural Architecture Encoding with Trans
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers Authors: Jaemin Cho, Abhay Zala, and Mohit Bansal (
SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers
SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers This repo contains our codes for the paper "No Parameters Left Behind: Sensitivity Gu
Rank-One Model Editing for Locating and Editing Factual Knowledge in GPT
Rank-One Model Editing (ROME) This repository provides an implementation of Rank-One Model Editing (ROME) on auto-regressive transformers (GPU-only).
Customised to detect objects automatically by a given model file(onnx)
LabelImg LabelImg is a graphical image annotation tool. It is written in Python and uses Qt for its graphical interface. Annotations are saved as XML
Convert Pytorch model to onnx or tflite, and the converted model can be visualized by Netron
Convert Pytorch model to onnx or tflite, and the converted model can be visualized by Netron
Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
English | 简体中文 | 繁體中文 | 한국어 State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained models
Official Implementation of "Transformers Can Do Bayesian Inference"
Official Code for the Paper "Transformers Can Do Bayesian Inference" We train Transformers to do Bayesian Prediction on novel datasets for a large var
YOLOv7 - Framework Beyond Detection
🔥🔥🔥🔥 YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥
ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX
ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX
spaCy-wrap: For Wrapping fine-tuned transformers in spaCy pipelines
spaCy-wrap: For Wrapping fine-tuned transformers in spaCy pipelines spaCy-wrap is minimal library intended for wrapping fine-tuned transformers from t
In this workshop we will be exploring NLP state of the art transformers, with SOTA models like T5 and BERT, then build a model using HugginFace transformers framework.
Transformers are all you need In this workshop we will be exploring NLP state of the art transformers, with SOTA models like T5 and BERT, then build a
Optical character recognition for Japanese text, with the main focus being Japanese manga
Manga OCR Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses a custom end-to-end model built with Tran
DocEnTr: An end-to-end document image enhancement transformer
DocEnTR Description Pytorch implementation of the paper DocEnTr: An End-to-End Document Image Enhancement Transformer. This model is implemented on to
Deep ViT Features as Dense Visual Descriptors
dino-vit-features [paper] [project page] Official implementation of the paper "Deep ViT Features as Dense Visual Descriptors". We demonstrate the effe
Goal of the project : Detecting Temporal Boundaries in Sign Language videos
MVA RecVis course final project : Goal of the project : Detecting Temporal Boundaries in Sign Language videos. Sign language automatic indexing is an
A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation".
Dual-Contrastive-Learning A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation". Y
Leaf: Multiple-Choice Question Generation
Leaf: Multiple-Choice Question Generation Easy to use and understand multiple-choice question generation algorithm using T5 Transformers. The applicat
Improved Fitness Optimization Landscapes for Sequence Design
ReLSO Improved Fitness Optimization Landscapes for Sequence Design Description Citation How to run Training models Original data source Description In
Pytorch implementation of the paper DocEnTr: An End-to-End Document Image Enhancement Transformer.
DocEnTR Description Pytorch implementation of the paper DocEnTr: An End-to-End Document Image Enhancement Transformer. This model is implemented on to
Vision transformers (ViTs) have found only limited practical use in processing images
CXV Convolutional Xformers for Vision Vision transformers (ViTs) have found only limited practical use in processing images, in spite of their state-o
Predict the spans of toxic posts that were responsible for the toxic label of the posts
toxic-spans-detection An attempt at the SemEval 2021 Task 5: Toxic Spans Detection. The Toxic Spans Detection task of SemEval2021 required participant
Sinkformers: Transformers with Doubly Stochastic Attention
Code for the paper : "Sinkformers: Transformers with Doubly Stochastic Attention" Paper You will find our paper here. Compat This package has been dev