Jack  Shaw

Jack Shaw

1666642380

Masked Siamese Networks for Label-Efficient Learning in Python

MSN Masked Siamese Networks

This repo provides a PyTorch implementation of MSN (Masked Siamese Networks), as described in the paper Masked Siamese Networks for Label-Efficient Learning.

MSN is a self-supervised learning framework that leverages the idea of mask-denoising while avoiding pixel and token-level reconstruction. Given two views of an image, MSN randomly masks patches from one view while leaving the other view unchanged. The objective is to train a neural network encoder, parametrized with a vision transformer (ViT), to output similar embeddings for the two views. In this procedure, MSN does not predict the masked patches at the input level, but rather performs the denoising step implicitly at the representation level by ensuring that the representation of the masked input matches the representation of the unmasked one.

Low-shot evaluation on ImageNet-1K

Low-shot Evaluation of self-supervised models, pre-trained on ImageNet-1K. (Left) MSN surpasses the previous 800M parameter state-of-the-art. (Right) MSN achieves good classification performance using less labels than current mask-based auto-encoders. 

Visualizations

We can use the RCDM framework of Bordes et al., 2021 to qualitatively demonstrates the effectiveness of the MSN denoising process.

First column: original image. Second column: image with 90% of patches masked used to compute representations of an MSN pre-trained ViT-L/7 encoder. Other columns: RCDM sampling from generative model conditioned on MSN representation of masked image. Qualities that vary across samples represent information that is not contained in the pre-trained representation; e.g., in this case, MSN discards background, pose, and lighting information. Qualities that are common across samples represent information contained in the pre-trained representation. Even with high-masking ratio, MSN retains semantic information about the images. 

Pre-trained models

ViT Small [16x16]download [800 epochs]
ViT Base [16x16]download [600 epochs]
ViT Large [16x16]download [600 epochs]
ViT Base [4x4]download [300 epochs]
ViT Large [7x7]download [200 epochs]

Running MSN self-supervised pre-training

Config files

All experiment parameters are specified in config files (as opposed to command-line-arguments). Config files make it easier to keep track of different experiments, as well as launch batches of jobs at a time. See the configs/ directory for example config files.

Requirements

  • Python 3.8 (or newer)
  • PyTorch install 1.11.0 (older versions may work too)
  • torchvision
  • Other dependencies: PyYaml, numpy, opencv, submitit, cyanure

Single-GPU training

Our implementation starts from the main.py, which parses the experiment config file and runs the msn pre-training locally on a multi-GPU (or single-GPU) machine. For example, to run on GPUs "0","1", and "2" on a local machine, use the command:

python main.py \
  --fname configs/pretrain/msn_vits16.yaml \
  --devices cuda:0 cuda:1 cuda:2

Multi-GPU training

In the multi-GPU setting, the implementation starts from main_distributed.py, which, in addition to parsing the config file, also allows for specifying details about distributed training. For distributed training, we use the popular open-source submitit tool and provide examples for a SLURM cluster. Feel free to edit main_distributed.py for your purposes to specify a different procedure for launching a multi-GPU job on a cluster.

For example, to pre-train with MSN on 16 GPUs using the pre-training experiment configs specificed inside configs/pretrain/msn_vits16.yaml, run:

python main_distributed.py \
  --fname configs/pretrain/msn_vits16.yaml \
  --folder $path_to_save_submitit_logs \
  --partition $slurm_partition \
  --nodes 2 --tasks-per-node 8 \
  --time 1000

ImageNet-1K Logistic Regression Evaluation

Labeled Training Splits

For reproducibilty, we have pre-specified the labeled training images as .txt files in the imagenet_subsets/ directory. Based on your specifications in your experiment's config file, our implementation will automatically use the images specified in one of these .txt files as the set of labeled images.

To run logistic regression on a pre-trained model using some labeled training split you can directly call the script from the command line:

python logistic_eval.py \
  --subset-path imagenet_subsets1/5imgs_class.txt \
  --root-path /datasets/ --image-folder imagenet_full_size/061417/ \
  --device cuda:0 \
  --pretrained $directory_containing_your_model \
  --fname $model_filename \
  --model-name deit_small \
  --penalty l2 \
  --lambd 0.0025

ImageNet-1K Linear Evaluation

To run linear evaluation on the entire ImageNet-1K dataset, use the main_distributed.py script and specify the --linear-eval flag.

For example, to evaluate MSN on 32 GPUs using the linear evaluation config specificed inside configs/eval/lineval_msn_vits16.yaml, run:

python main_distributed.py \
  --linear-eval \
  --fname configs/eval/lineval_msn_vits16.yaml \
  --folder $path_to_save_submitit_logs \
  --partition $slurm_partition \
  --nodes 4 --tasks-per-node 8 \
  --time 1000

ImageNet-1K Fine-Tuning Evaluation

For fine-tuning evaluation, we use the MAE codebase.

License

See the LICENSE file for details about the license under which this code is made available.

Citation

If you find this repository useful in your research, please consider giving a star :star: and a citation

@article{assran2022masked,
  title={Masked Siamese Networks for Label-Efficient Learning}, 
  author={Assran, Mahmoud, and Caron, Mathilde, and Misra, Ishan, and Bojanowski, Piotr, and Bordes, Florian and Vincent, Pascal, and Joulin, Armand, and Rabbat, Michael, and Ballas, Nicolas},
  journal={arXiv preprint arXiv:2204.07141},
  year={2022}
}

Download Details:

Author: facebookresearch
Source Code: https://github.com/facebookresearch/msn

License: View license

#python 

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Masked Siamese Networks for Label-Efficient Learning in Python
Ray  Patel

Ray Patel

1625843760

Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

  • revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
  • microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
  • Anaconda 4.2 – Anaconda is an opensource Python package

#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services

Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Sival Alethea

Sival Alethea

1624291780

Learn Python - Full Course for Beginners [Tutorial]

This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3

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Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#python #learn python #learn python for beginners #learn python - full course for beginners [tutorial] #python programmer #concepts in python

Shardul Bhatt

Shardul Bhatt

1626775355

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Art  Lind

Art Lind

1602968400

Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development