PyTorch for research
PyTorch Lightning — The PyTorch Keras for ML researchers. More control. Less boilerplate.
Github: https://github.com/williamFalcon/pytorch-lightning
#PyTorch #Research #OpenSource
PyTorch Lightning — The PyTorch Keras for ML researchers. More control. Less boilerplate.
Github: https://github.com/williamFalcon/pytorch-lightning
#PyTorch #Research #OpenSource
GitHub
GitHub - Lightning-AI/pytorch-lightning: Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes. - Lightning-AI/pytorch-lightning
spaCy meets PyTorch-Transformers: Fine-tune BERT, XLNet and GPT-2
Including pretrained models.
Link: https://explosion.ai/blog/spacy-pytorch-transformers
Pip:
#Transformers #SpaCy #NLP #NLU #PyTorch #Bert #XLNet #GPT
Including pretrained models.
Link: https://explosion.ai/blog/spacy-pytorch-transformers
Pip:
pip install spacy-pytorch-transformers#Transformers #SpaCy #NLP #NLU #PyTorch #Bert #XLNet #GPT
explosion.ai
spaCy meets Transformers: Fine-tune BERT, XLNet and GPT-2 · Explosion
Huge transformer models like BERT, GPT-2 and XLNet have set a new standard for accuracy on almost every NLP leaderboard. You can now use these models in spaCy, via a new interface library we've developed that connects spaCy to Hugging Face's awesome implementations.
Great collections of Data Science learning materials
The list includes free books and online courses on range of DS-related disciplines:
Machine learning (#ML)
Deep Learning (#DL)
Reinforcement learning (#RL)
#NLP
Tutorials on #Keras, #Tensorflow, #Torch, #PyTorch, #Theano
Notable researchers, papers and even #datasets. It is a great place to start reviewing your knowledge or learning something new.
Link: https://hackmd.io/@chanderA/aiguide
#wheretostart #entrylevel #novice #studycontent #studymaterials #books #MOOC #meta
The list includes free books and online courses on range of DS-related disciplines:
Machine learning (#ML)
Deep Learning (#DL)
Reinforcement learning (#RL)
#NLP
Tutorials on #Keras, #Tensorflow, #Torch, #PyTorch, #Theano
Notable researchers, papers and even #datasets. It is a great place to start reviewing your knowledge or learning something new.
Link: https://hackmd.io/@chanderA/aiguide
#wheretostart #entrylevel #novice #studycontent #studymaterials #books #MOOC #meta
GANs from Scratch 1: A deep introduction.
Great introduction and tutorial. With code in PyTorch and TensorFlow
Link: https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f
#tensorflow #pytorch #GAN #tutorial #entrylevel #novice #wheretostart
Great introduction and tutorial. With code in PyTorch and TensorFlow
Link: https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f
#tensorflow #pytorch #GAN #tutorial #entrylevel #novice #wheretostart
Video on how Facebook continues to develop its #Portal device
How #Facebook used Mask R-CNN, #PyTorch, and custom hardware integrations like foveated processing to improve Portal’s Smart Camera system.
Link: https://ai.facebook.com/blog/smart-camera-portal-advances/
#CV #DL #Segmentation
How #Facebook used Mask R-CNN, #PyTorch, and custom hardware integrations like foveated processing to improve Portal’s Smart Camera system.
Link: https://ai.facebook.com/blog/smart-camera-portal-advances/
#CV #DL #Segmentation
Meta
How we’ve advanced Smart Camera for new Portal video-calling devices
We’ve used Detectron2, Mask R-CNN, and custom hardware integrations like foveated processing in order to make additional speed and precision improvements in the computer vision models that power Smart Camera.
PyTorch 1.3 released
- named tensors support
- general availability of Google Cloud TPU support
- captum - SOTA tools to understand how the importance of specific neurons and layers affect predictions made by the models
- crypten - a new research tool for secure machine learning with PyTorch
- many other improvements
Official announce: https://pytorch.org/blog/pytorch-1-dot-3-adds-mobile-privacy-quantization-and-named-tensors/
Captum website: https://www.captum.ai
CrypTen code: https://github.com/facebookresearch/CrypTen
#DL #PyTorch #TPU #GCP #Captum #CrypTen
- named tensors support
- general availability of Google Cloud TPU support
- captum - SOTA tools to understand how the importance of specific neurons and layers affect predictions made by the models
- crypten - a new research tool for secure machine learning with PyTorch
- many other improvements
Official announce: https://pytorch.org/blog/pytorch-1-dot-3-adds-mobile-privacy-quantization-and-named-tensors/
Captum website: https://www.captum.ai
CrypTen code: https://github.com/facebookresearch/CrypTen
#DL #PyTorch #TPU #GCP #Captum #CrypTen
pytorch.org
An open source machine learning framework that accelerates the path from research prototyping to production deployment.
🎓 Reinforcement Learning Course from OpenAI
Reinforcement Learning becoming significant part of the data scientist toolbox.
OpenAI created and published one of the best courses in #RL. Algorithms implementation written in #Tensorflow.
But if you are more comfortable with #PyTorch, we have found #PyTorch implementation of this algs
OpenAI Course: https://spinningup.openai.com/en/latest/
Tensorflow Code: https://github.com/openai/spinningup
PyTorch Code: https://github.com/kashif/firedup
#MOOC #edu #course #OpenAI
Reinforcement Learning becoming significant part of the data scientist toolbox.
OpenAI created and published one of the best courses in #RL. Algorithms implementation written in #Tensorflow.
But if you are more comfortable with #PyTorch, we have found #PyTorch implementation of this algs
OpenAI Course: https://spinningup.openai.com/en/latest/
Tensorflow Code: https://github.com/openai/spinningup
PyTorch Code: https://github.com/kashif/firedup
#MOOC #edu #course #OpenAI
GitHub
GitHub - openai/spinningup: An educational resource to help anyone learn deep reinforcement learning.
An educational resource to help anyone learn deep reinforcement learning. - openai/spinningup
Implementing Transfer Learning in PyTorch
Fine-tuning and feature extraction with PyTorch
Link: https://medium.com/analytics-vidhya/transfer-learning-in-pytorch-f7736598b1ed
#PyTorch #novice #entrylevel #beginner
Fine-tuning and feature extraction with PyTorch
Link: https://medium.com/analytics-vidhya/transfer-learning-in-pytorch-f7736598b1ed
#PyTorch #novice #entrylevel #beginner
Medium
Implementing Transfer Learning in PyTorch
Transfer Learning is a technique where a model trained for a certain task is used for another similar task.
Free book on #PyTorch
Book with all the information required to work with one of the most popular #dl frameworks.
Link: https://twitter.com/PyTorch/status/1197603717144432640?s=20
#book #manual
Book with all the information required to work with one of the most popular #dl frameworks.
Link: https://twitter.com/PyTorch/status/1197603717144432640?s=20
#book #manual
Twitter
PyTorch
To help developers get started with PyTorch, we’re making the 'Deep Learning with PyTorch' book, written by Luca Antiga and Eli Stevens, available for free to the community: https://t.co/KH52NW0Itl
Neighbourhood Components Analysis
a PyTorch implementation of Neighbourhood Components Analysis
NCA learns a linear transformation of the dataset such that the expected leave-one-out performance of kNN in the transformed space is maximized.
The authors propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leave-one-out KNN score on the training set.
It can also learn low-dimensional linear embedding of labeled data that can be used for data visualization and fast classification. Unlike other methods, this classification model is non-parametric, making no assumptions about the shape of the class distributions or the boundaries between them.
The performance of the method is demonstrated on several data sets, both for metric learning and linear dimensionality reduction.
paper (only pdf): https://www.cs.toronto.edu/~hinton/absps/nca.pdf
github: https://github.com/kevinzakka/nca
#kNN #pca #nca #PyTorch
a PyTorch implementation of Neighbourhood Components Analysis
NCA learns a linear transformation of the dataset such that the expected leave-one-out performance of kNN in the transformed space is maximized.
The authors propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leave-one-out KNN score on the training set.
It can also learn low-dimensional linear embedding of labeled data that can be used for data visualization and fast classification. Unlike other methods, this classification model is non-parametric, making no assumptions about the shape of the class distributions or the boundaries between them.
The performance of the method is demonstrated on several data sets, both for metric learning and linear dimensionality reduction.
paper (only pdf): https://www.cs.toronto.edu/~hinton/absps/nca.pdf
github: https://github.com/kevinzakka/nca
#kNN #pca #nca #PyTorch
StarGAN v2 code release on GitHub
The better news is if you put a human into the animal model you do in fact get out a feline version of the human, and it's even wearing a suit.
GitHub: https://github.com/clovaai/stargan-v2
ArXiV: https://arxiv.org/abs/1912.01865
YouTube: https://www.youtube.com/watch?v=0EVh5Ki4dIY&feature=youtu.be
#GAN #StarGAN #PyTorch
The better news is if you put a human into the animal model you do in fact get out a feline version of the human, and it's even wearing a suit.
GitHub: https://github.com/clovaai/stargan-v2
ArXiV: https://arxiv.org/abs/1912.01865
YouTube: https://www.youtube.com/watch?v=0EVh5Ki4dIY&feature=youtu.be
#GAN #StarGAN #PyTorch
Automatic product tagging on photoes on Facebook Pages
#FacebookAI released an improvement aiming at enhancing shopping platform.
Post: https://ai.facebook.com/blog/powered-by-ai-advancing-product-understanding-and-building-new-shopping-experiences
Paper: https://scontent-arn2-1.xx.fbcdn.net/v/t39.8562-6/99353320_565175057533429_3886205100842024960_n.pdf
#GrokNet #DL #segmentation #PyTorch
#FacebookAI released an improvement aiming at enhancing shopping platform.
Post: https://ai.facebook.com/blog/powered-by-ai-advancing-product-understanding-and-building-new-shopping-experiences
Paper: https://scontent-arn2-1.xx.fbcdn.net/v/t39.8562-6/99353320_565175057533429_3886205100842024960_n.pdf
#GrokNet #DL #segmentation #PyTorch
Live U-Net implementation online session today
Famous Abhishek Thakur (First 4x GM on Kaggle) is going to show you how to implement the original U-Net with #PyTorch.
Session starts in 4 hours from now (at 6PM CET / 9.30PM IST), make sure you turned the notifications on if you are interested.
YouTube Link: https://www.youtube.com/watch?v=u1loyDCoGbE
#Livecoding #Unet
Famous Abhishek Thakur (First 4x GM on Kaggle) is going to show you how to implement the original U-Net with #PyTorch.
Session starts in 4 hours from now (at 6PM CET / 9.30PM IST), make sure you turned the notifications on if you are interested.
YouTube Link: https://www.youtube.com/watch?v=u1loyDCoGbE
#Livecoding #Unet
YouTube
Implementing original U-Net from scratch using PyTorch
In this video, I show you how to implement original UNet paper using PyTorch. UNet paper can be found here: https://arxiv.org/abs/1505.04597
Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :)
To buy my…
Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :)
To buy my…
ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network
The authors propose a set of design principles that improves model performance significantly based on the analysis of representation bottlenecks.
Authors think that commonly used architectures have a representation bottleneck and try to fix it by expanding channel size, using more expand layers, and better activation functions. This also improves the performance of models on ImageNet and good results on transfer learning on classification and object detection.
Authors hope that their design ideas could be used by NAS to create even better models.
Paper: https://arxiv.org/abs/2007.00992
Code: https://github.com/clovaai/rexnet
#deeplearning #pretraining #transferlearning #computervision #pytorch
The authors propose a set of design principles that improves model performance significantly based on the analysis of representation bottlenecks.
Authors think that commonly used architectures have a representation bottleneck and try to fix it by expanding channel size, using more expand layers, and better activation functions. This also improves the performance of models on ImageNet and good results on transfer learning on classification and object detection.
Authors hope that their design ideas could be used by NAS to create even better models.
Paper: https://arxiv.org/abs/2007.00992
Code: https://github.com/clovaai/rexnet
#deeplearning #pretraining #transferlearning #computervision #pytorch
Funnel Activation for Visual Recognition
Authors offer a new activation function for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by adding a negligible overhead of spatial condition.
Extensive experiments on COCO, ImageNet and CityScape show significant improvement and robustness.
Paper: https://arxiv.org/abs/2007.11824
Code: https://github.com/megvii-model/FunnelAct
#deeplearning #activationfunction #computervision #pytorch
Authors offer a new activation function for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by adding a negligible overhead of spatial condition.
Extensive experiments on COCO, ImageNet and CityScape show significant improvement and robustness.
Paper: https://arxiv.org/abs/2007.11824
Code: https://github.com/megvii-model/FunnelAct
#deeplearning #activationfunction #computervision #pytorch
🏥Self-supervised Learning for Medical images
Due to standard imaging procedures, medical images (X-ray, CT scans, etc) are usually well aligned.
This paper gives an opportunity to utilize such an alignment to automatically connect similar pairs of images for training.
GitHub: https://github.com/fhaghighi/TransVW
ArXiV: https://arxiv.org/abs/2102.10680
#biolearning #medical #dl #pytorch #keras
Due to standard imaging procedures, medical images (X-ray, CT scans, etc) are usually well aligned.
This paper gives an opportunity to utilize such an alignment to automatically connect similar pairs of images for training.
GitHub: https://github.com/fhaghighi/TransVW
ArXiV: https://arxiv.org/abs/2102.10680
#biolearning #medical #dl #pytorch #keras
GitHub
GitHub - fhaghighi/TransVW: Official Keras & PyTorch Implementation and Pre-trained Models for TransVW
Official Keras & PyTorch Implementation and Pre-trained Models for TransVW - fhaghighi/TransVW
Implementing original #UNet paper using #PyTorch
Video tutorial on how to code your own neural network from scratch.
Link: https://www.youtube.com/watch?v=u1loyDCoGbE&t=1s
Paper: https://arxiv.org/abs/1505.04597
Video tutorial on how to code your own neural network from scratch.
Link: https://www.youtube.com/watch?v=u1loyDCoGbE&t=1s
Paper: https://arxiv.org/abs/1505.04597
YouTube
Implementing original U-Net from scratch using PyTorch
In this video, I show you how to implement original UNet paper using PyTorch. UNet paper can be found here: https://arxiv.org/abs/1505.04597
Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :)
To buy my…
Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :)
To buy my…
🦜 Hi!
We are the first Telegram Data Science channel.
Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.
Ultimate Posts
* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda
Open Data Science
ODS.ai is an international community of people anyhow related to Data Science.
Website: https://ods.ai
Hashtags
Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.
#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization
Chats
- Data Science Chat https://t.me/datascience_chat
- ODS Slack through invite form at website
ODS resources
* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai
Feedback and Contacts
You are welcome to reach administration through telegram bot: @opendatasciencebot
We are the first Telegram Data Science channel.
Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.
Ultimate Posts
* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda
Open Data Science
ODS.ai is an international community of people anyhow related to Data Science.
Website: https://ods.ai
Hashtags
Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.
#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization
Chats
- Data Science Chat https://t.me/datascience_chat
- ODS Slack through invite form at website
ODS resources
* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai
Feedback and Contacts
You are welcome to reach administration through telegram bot: @opendatasciencebot
GitHub
ultimate_posts/where_to_start at master · open-data-science/ultimate_posts
Ultimate posts for opendatascience telegram channel - open-data-science/ultimate_posts
Segment Anything
The Segment Anything project aims to democratize image segmentation in computer vision, a core task used across various applications such as scientific imagery analysis and photo editing. Traditionally, accurate segmentation models require specialized expertise, AI training infrastructure, and large amounts of annotated data. This project introduces a new task, dataset, and model for image segmentation to overcome these challenges and make segmentation more accessible.
The researchers are releasing the Segment Anything Model (SAM) and the Segment Anything 1-Billion mask dataset (SA-1B), the largest segmentation dataset to date. These resources will enable a wide range of applications and further research into foundational models for computer vision. The SA-1B dataset is available for research purposes, while the SAM is provided under the permissive Apache 2.0 open license. Users can explore the demo to try SAM with their own images.
Paper link: https://arxiv.org/abs/2304.02643
Code link: https://github.com/facebookresearch/segment-anything
Demo link: https://segment-anything.com/demo
Blogpost link: https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/
Dataset link: https://ai.facebook.com/datasets/segment-anything/
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-sam
#deeplearning #cv #pytorch #imagesegmentation #dataset
The Segment Anything project aims to democratize image segmentation in computer vision, a core task used across various applications such as scientific imagery analysis and photo editing. Traditionally, accurate segmentation models require specialized expertise, AI training infrastructure, and large amounts of annotated data. This project introduces a new task, dataset, and model for image segmentation to overcome these challenges and make segmentation more accessible.
The researchers are releasing the Segment Anything Model (SAM) and the Segment Anything 1-Billion mask dataset (SA-1B), the largest segmentation dataset to date. These resources will enable a wide range of applications and further research into foundational models for computer vision. The SA-1B dataset is available for research purposes, while the SAM is provided under the permissive Apache 2.0 open license. Users can explore the demo to try SAM with their own images.
Paper link: https://arxiv.org/abs/2304.02643
Code link: https://github.com/facebookresearch/segment-anything
Demo link: https://segment-anything.com/demo
Blogpost link: https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/
Dataset link: https://ai.facebook.com/datasets/segment-anything/
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-sam
#deeplearning #cv #pytorch #imagesegmentation #dataset
DINOv2: Learning Robust Visual Features without Supervision
Get ready for a game-changer in computer vision! Building on the groundbreaking achievements in natural language processing, foundation models are revolutionizing the way we use images in various systems. By generating all-purpose visual features that excel across diverse image distributions and tasks without finetuning, these models are set to redefine the field.
The researchers behind this work have combined cutting-edge techniques to scale pretraining in terms of data and model size, turbocharging the training process like never before. They've devised an ingenious automatic pipeline to create a rich, diverse, and curated image dataset, setting a new standard in the self-supervised literature. To top it off, they've trained a colossal ViT model with a staggering 1 billion parameters and distilled it into a series of smaller, ultra-efficient models. These models outshine the best available all-purpose features, OpenCLIP, on most benchmarks at both image and pixel levels.
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-dinov2
Project link: https://dinov2.metademolab.com/
#deeplearning #cv #pytorch #imagesegmentation #sota #pretraining
Get ready for a game-changer in computer vision! Building on the groundbreaking achievements in natural language processing, foundation models are revolutionizing the way we use images in various systems. By generating all-purpose visual features that excel across diverse image distributions and tasks without finetuning, these models are set to redefine the field.
The researchers behind this work have combined cutting-edge techniques to scale pretraining in terms of data and model size, turbocharging the training process like never before. They've devised an ingenious automatic pipeline to create a rich, diverse, and curated image dataset, setting a new standard in the self-supervised literature. To top it off, they've trained a colossal ViT model with a staggering 1 billion parameters and distilled it into a series of smaller, ultra-efficient models. These models outshine the best available all-purpose features, OpenCLIP, on most benchmarks at both image and pixel levels.
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-dinov2
Project link: https://dinov2.metademolab.com/
#deeplearning #cv #pytorch #imagesegmentation #sota #pretraining
