pytorch-metric-learning | easiest way to use deep metric learning | Machine Learning library

by KevinMusgrave Python Version: 2.5.0 License: MIT

X-Ray Key Features Code Snippets(4)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | pytorch-metric-learning Summary

pytorch-metric-learning is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. pytorch-metric-learning has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install pytorch-metric-learning' or download it from GitHub, PyPI.

See the examples folder for notebooks you can download or run on Google Colab.

Support

Quality

Security

License

Reuse

Support

pytorch-metric-learning has a medium active ecosystem.

It has 5290 star(s) with 616 fork(s). There are 67 watchers for this library.

There were 3 major release(s) in the last 6 months.

There are 48 open issues and 409 have been closed. On average issues are closed in 6 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pytorch-metric-learning is 2.5.0

Quality

pytorch-metric-learning has 0 bugs and 0 code smells.

Security

pytorch-metric-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pytorch-metric-learning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pytorch-metric-learning is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

pytorch-metric-learning releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

pytorch-metric-learning saves you 4976 person hours of effort in developing the same functionality from scratch.

It has 12906 lines of code, 833 functions and 173 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed pytorch-metric-learning and discovered the below as its top functions. This is intended to give you an instant insight into pytorch-metric-learning implemented functionality, and help decide if they suit your requirements.

Forward computation
Creates a tuple containing the indices
Adds embeddings and labels to memory
Resets the stats on the input_obj
Compute loss
Return the margin of the image
Compute the loss of a weight regularizer
Mine the embeddings
Return the negatives of a given matrix
Calculate weight weights
Computes the loss of the model
Compute the loss of the discriminator
Remove self - comparisons
Compute the loss of the loss
Calculate the mean average precision
Calculate r precision
Compute the reduction
Calculate the mean reciprocal rank
Calculate mean average precision
Compute the loss
Compute the loss between embeddings
Creates a function that runs after training
Return a new RecordKeeper instance
Calculate the loss
Compute the violation loss
Compute the loss between two pairs

Get all kandi verified functions for this library.

pytorch-metric-learning Key Features

No Key Features are available at this moment for pytorch-metric-learning.

pytorch-metric-learning Examples and Code Snippets

Getting Started

Python

Lines of Code : 11

License : Permissive (MIT)

Copy

python tools/train_metric.py \
    --cfg configs/metric/R-50-1x64d_step_8gpu.yaml \
    OUT_DIR ./output \
    PORT 12001 \
    TRAIN.WEIGHTS path/to/pretrainedmodel

set ${total_num} = n*(gpu_cards)
sh tools/metric/eval/infer.sh
python search.py sea

Citing this library

pypi

Lines of Code : 8

License : No License

Copy

@misc{musgrave2020pytorch,
    title={PyTorch Metric Learning},
    author={Kevin Musgrave and Serge Belongie and Ser-Nam Lim},
    year={2020},
    eprint={2008.09164},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Installation,pymetric

Python

Lines of Code : 4

License : Permissive (MIT)

Copy

# PYMETRIC=/path/to/clone/pymetric
git clone https://github.com/feymanpriv/pymetric $PYMETRIC

pip install -r $PYMETRIC/requirements.txt

cd $PYMETRIC && export PYTHONPATH=`pwd`:$PYTHONPATH

Could not install pytorch to my anaconda virtual environment

Python

Lines of Code : 2

License : Strong Copyleft (CC BY-SA 4.0)

Copy

pip install torch===1.3.1 -f https://download.pytorch.org/whl/torch_stable.html

Community Discussions

Trending Discussions on pytorch-metric-learning

Could not install pytorch to my anaconda virtual environment

QUESTION

Could not install pytorch to my anaconda virtual environment

Asked 2020-May-19 at 16:36

I am following the OpenAI's spinningUp tutorial and I stucked in the installation part of the project. I am using Anaconda as said and when I do:

...

ANSWER

Answered 2020-May-19 at 14:50

torch==1.3 on pypi only has files for linux and macOS, see here.

You will need to install it seperately using the index from the torch website:

Source https://stackoverflow.com/questions/61893677

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pytorch-metric-learning

You can install using 'pip install pytorch-metric-learning' or download it from GitHub, PyPI.
You can use pytorch-metric-learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.