ResNeSt | ResNeSt : Split-Attention Networks | Machine Learning library

by zhanghang1989 Python Version: 0.0.6b20230402 License: Apache-2.0

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | ResNeSt Summary

ResNeSt is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. ResNeSt has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However ResNeSt has 1 bugs. You can install using 'pip install ResNeSt' or download it from GitHub, PyPI.

Split-Attention Network, A New ResNet Variant. It significantly boosts the performance of downstream models such as Mask R-CNN, Cascade R-CNN and DeepLabV3.

Support

Quality

Security

License

Reuse

Support

ResNeSt has a medium active ecosystem.

It has 3126 star(s) with 497 fork(s). There are 57 watchers for this library.

It had no major release in the last 12 months.

There are 56 open issues and 82 have been closed. On average issues are closed in 33 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of ResNeSt is 0.0.6b20230402

Quality

ResNeSt has 1 bugs (1 blocker, 0 critical, 0 major, 0 minor) and 82 code smells.

Security

ResNeSt has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ResNeSt code analysis shows 0 unresolved vulnerabilities.

There are 16 security hotspots that need review.

License

ResNeSt is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

ResNeSt 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.

ResNeSt saves you 1047 person hours of effort in developing the same functionality from scratch.

It has 2374 lines of code, 147 functions and 26 files.

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

Top functions reviewed by kandi - BETA

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

Main worker function
Save a checkpoint to a directory
Creates a criterion criterion
Return a stream for a file
Downloads data from coco
Download file
Runs the inference with TTA
Test accuracy
Get training data
A list of autogenerates for images in the image
Create a single layer
Calculate the input size
Get data from rec_val
Resnest 50 tensorflow model
Convert a FASTA file to fasta format
Simplified resnest50
Construct a Resnest50 model
Produce a Resnest50 model
Create a resnest 50 model
Train the Gluon network
Resnest50 fast
Compute accuracy
Build a resnest flattened FPN core
Test the test function
Parse the command line arguments
Augment the list of images
Extracts train classes from a tar file

Get all kandi verified functions for this library.

ResNeSt Key Features

No Key Features are available at this moment for ResNeSt.

ResNeSt Examples and Code Snippets

Divide and Co-training,Installation,Prepare data

Python

Lines of Code : 34

License : Permissive (Apache-2.0)

Copy

${HOME}
├── dataset             (save the dataset) 
│   │
│   ├── cifar           (dir of CIFAR dataset)
│   ├── imagenet        (dir of ImageNet dataset)
│   └── svhn            (dir of SVHN dataset)
│
├── models              (save the output checkp

SplitNet: Divide and Co-training,Installation,Prepare data

Python

Lines of Code : 34

License : Permissive (Apache-2.0)

Copy

${HOME}
├── dataset             (save the dataset) 
│   │
│   ├── cifar           (dir of CIFAR dataset)
│   ├── imagenet        (dir of ImageNet dataset)
│   └── svhn            (dir of SVHN dataset)
│
├── models              (save the output checkp

ResNeSt-How do I use this model on an image?

Python

Lines of Code : 33

License : Permissive (Apache-2.0)

Copy

import timm
model = timm.create_model('resnest101e', pretrained=True)
model.eval()

import urllib
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

config = resolve_data_config(

Community Discussions

Trending Discussions on ResNeSt

The grammar explanation of torch[cpuType]

QUESTION

The grammar explanation of torch[cpuType]

Asked 2017-Aug-04 at 09:05

I first see the usage in lua like torch[cpuType] in the file dataloader.lua of fb.resnest.torch:

...

ANSWER

Answered 2017-Jul-18 at 18:12

From my knowledge in pytorch, which is pretty much very similar to Lua Torch (I tried lua torch too), I would say it specifies where you want this tensor to be stored. Note that torch cannot perform an operation stored two different processing unit. There are methods to move data between cpu (net≈ß.cpu()) and gpu [net.cuda()].

Source https://stackoverflow.com/questions/45084936

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

Vulnerabilities

No vulnerabilities reported

Install ResNeSt

You can install using 'pip install ResNeSt' or download it from GitHub, PyPI.
You can use ResNeSt 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.

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 .

Find more information at: