Unsupervised-Classification | [ ECCV | Machine Learning library
kandi X-RAY | Unsupervised-Classification Summary
kandi X-RAY | Unsupervised-Classification Summary
Unsupervised-Classification is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. Unsupervised-Classification has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However Unsupervised-Classification has a Non-SPDX License. You can download it from GitHub.
Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision. Several recent approaches have tried to tackle this problem in an end-to-end fashion. In this paper, we deviate from recent works, and advocate a two-step approach where feature learning and clustering are decoupled. We outperform state-of-the-art methods by large margins, in particular +26.6% on CIFAR10, +25.0% on CIFAR100-20 and +21.3% on STL10 in terms of classification accuracy. Our method is the first to perform well on ImageNet (1000 classes). Check out the benchmarks on the Papers-with-code website for Image Clustering and Unsupervised Image Classification.
Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision. Several recent approaches have tried to tackle this problem in an end-to-end fashion. In this paper, we deviate from recent works, and advocate a two-step approach where feature learning and clustering are decoupled. We outperform state-of-the-art methods by large margins, in particular +26.6% on CIFAR10, +25.0% on CIFAR100-20 and +21.3% on STL10 in terms of classification accuracy. Our method is the first to perform well on ImageNet (1000 classes). Check out the benchmarks on the Papers-with-code website for Image Clustering and Unsupervised Image Classification.
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Support
Unsupervised-Classification has a medium active ecosystem.
It has 1191 star(s) with 251 fork(s). There are 52 watchers for this library.
It had no major release in the last 6 months.
There are 14 open issues and 111 have been closed. On average issues are closed in 23 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of Unsupervised-Classification is current.
Quality
Unsupervised-Classification has 0 bugs and 0 code smells.
Security
Unsupervised-Classification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Unsupervised-Classification code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Unsupervised-Classification has a Non-SPDX License.
Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
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Unsupervised-Classification releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
It has 2136 lines of code, 140 functions and 25 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Unsupervised-Classification and discovered the below as its top functions. This is intended to give you an instant insight into Unsupervised-Classification implemented functionality, and help decide if they suit your requirements.
- Get a model for a given model
- Resnet50
- Scans the given training loader
- Update the statistics
- Set the features to the given device
- Displays a batch
- Compute predictions for a given model
- Get feature dimensions from backbone
- Create a config dictionary
- Create a directory if it does not exist
- Return train transformations
- Get train dataset
- Evaluate the Hungarian model
- Compute the density of the features
- Get the topk features for the given features
- Returns a Dataset
- Evaluate the model
- Compute the loss between weakly augmented anchors
- Train model
- Performs self - label training
- Compute the similarity between anchors
- Returns a torch optimizer
- Adjust the learning rate
- Mine the topk nearest neighbors
- Return a list of all the layers
- Returns a criterion for a given classification
Get all kandi verified functions for this library.
Unsupervised-Classification Key Features
No Key Features are available at this moment for Unsupervised-Classification.
Unsupervised-Classification Examples and Code Snippets
SegOptim,Package description
R
Lines of Code : 9
License : No License
Lines of Code : 9License : No License
Copy
# !!Due to an issue with the NLMR package it is necessary to install it first!!
remotes::install_github("ropensci/NLMR")
# Now let's install SegOptim (with the last updates)
remotes::install_github("joaofgoncalves/SegOptim", ref="experimental")
This repository is created to implement Unsupervised classification of MNIST dataset using SNN,Observations so far:-
PythonLines of Code : 4License : No License
Lines of Code : 4License : No LicenseCopyNumber of input neurons : 784 (image 28x28 -> 784 x 1 vector)
Number of hidden neurons : 0
Number of output neurons : 800 neurons trained on 80 samples of each digit
Classification accuracy on MNIST Test set = 71.49%
Visualising high-dimensional data,Tutorial notes for useR! 2019
RLines of Code : 2License : Permissive (MIT)
Lines of Code : 2License : Permissive (MIT)Copyinstall.packages(c("knitr", "tidyverse", "here", "nullabor", "forecast", "readxl", "GGally", "broom", "plotly", "tourr", "spinifex", "geozoo", "mvtnorm", "randomForest", "RColorBrewer"))
remotes::install_github("wmurphyrd/fiftystater")
Community Discussions
Trending Discussions on Unsupervised-Classification
QUESTION
A weird requirements.txt format
Asked 2021-Oct-17 at 09:29
I downloaded a requirements.txt file from a GitHub repository, but it appears to be little different than the normal format of requirements.txt file.
- Can you tell me how the author generated this kind of
requirements.txtfile? Which tools did they use? - How can I use this particular file format to instantiate the Python environment? I have tried executing the commands
conda install --file requirements.txtandpip install -r requirements.txton a Windows ‘ machine, but to no avail.
https://github.com/wvangansbeke/Unsupervised-Classification/blob/master/requirements.txt
...ANSWER
Answered 2021-Oct-17 at 01:46This looks like a conda environment.yml file. It can be used to create a conda environment, like so
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Unsupervised-Classification
The code runs with recent Pytorch versions, e.g. 1.4. Assuming Anaconda, the most important packages can be installed as:. We refer to the requirements.txt file for an overview of the packages in the environment we used to produce our results.
The following files need to be adapted in order to run the code on your own machine:. Our experimental evaluation includes the following datasets: CIFAR10, CIFAR100-20, STL10 and ImageNet. The ImageNet dataset should be downloaded separately and saved to the path described in utils/mypath.py. Other datasets will be downloaded automatically and saved to the correct path when missing.
Change the file paths to the datasets in utils/mypath.py, e.g. /path/to/cifar10.
Specify the output directory in configs/env.yml. All results will be stored under this directory.
The following files need to be adapted in order to run the code on your own machine:. Our experimental evaluation includes the following datasets: CIFAR10, CIFAR100-20, STL10 and ImageNet. The ImageNet dataset should be downloaded separately and saved to the path described in utils/mypath.py. Other datasets will be downloaded automatically and saved to the correct path when missing.
Change the file paths to the datasets in utils/mypath.py, e.g. /path/to/cifar10.
Specify the output directory in configs/env.yml. All results will be stored under this directory.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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