pytorch-cifar | 95.47 % on CIFAR10 with PyTorch | Data Visualization library

by kuangliu Python Version: Current License: MIT

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

kandi X-RAY | pytorch-cifar Summary

pytorch-cifar is a Python library typically used in Analytics, Data Visualization, Pytorch applications. pytorch-cifar has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However pytorch-cifar build file is not available. You can download it from GitHub.

95.47% on CIFAR10 with PyTorch

Support

Quality

Security

License

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Support

pytorch-cifar has a medium active ecosystem.

It has 5309 star(s) with 2033 fork(s). There are 81 watchers for this library.

It had no major release in the last 6 months.

There are 88 open issues and 44 have been closed. On average issues are closed in 21 days. There are 17 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pytorch-cifar is current.

Quality

pytorch-cifar has 0 bugs and 0 code smells.

Security

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

pytorch-cifar code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pytorch-cifar 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-cifar releases are not available. You will need to build from source code and install.

pytorch-cifar has no build file. You will be need to create the build yourself to build the component from source.

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

pytorch-cifar saves you 751 person hours of effort in developing the same functionality from scratch.

It has 1731 lines of code, 170 functions and 21 files.

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

Top functions reviewed by kandi - BETA

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

Run the test
A DenseNet with CIFAR
A SENet
Print a progress bar
Shortcut block
Factory function for PNASNet
Shortcut for ResNet
Shuffle network
DPN core
Factory for RegNetX
Efficient network
Format time in human readable format
Forward the convolution
Drop the input tensor
Swish x with sigmoid
Test resNeXT
ResneX Tensor
Train the network
Forward convolutional layer
Compute the weighted average of an image

Get all kandi verified functions for this library.

pytorch-cifar Key Features

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

pytorch-cifar Examples and Code Snippets

Distributed P2P Learning Implementation,PyTorch Integration (How-To)

Python

Lines of Code : 16

License : Permissive (BSD-3-Clause)

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# Create a connection to the cluster
# the config file contains the list of named nodes in the cluster, and name identifies which node are we.
conn = DpwaPyTorchAdapter(net, name, config_file)

# Training loop
for batch in training_samples:
    # 1.

in PyTorch

Python

Lines of Code : 14

License : Permissive (WTFPL)

Copy

python main.py [-h] [--lr LR] [--steps STEPS] [--gpu] [--fp16] [--loss_scaling] [--model MODEL]

PyTorch (FP16) CIFAR10 Training

optional arguments:
  -h, --help            Show this help message and exit
  --lr LR               Learning Rate
  --st

Pytorch CIFAR Models,Use Models with Pytorch Hub

Python

Lines of Code : 5

License : Permissive (BSD-3-Clause)

Copy

import torch
model = torch.hub.load("chenyaofo/pytorch-cifar-models", "cifar10_resnet20", pretrained=True)

import torch
from pprint import pprint
pprint(torch.hub.list("chenyaofo/pytorch-cifar-models", force_reload=True))

Community Discussions

Trending Discussions on pytorch-cifar

I can't import torchvision

Batch size reduces accuracy of ensemble of pretrained CNNs

IndexError: list index out of range in prediction of images

QUESTION

I can't import torchvision

Asked 2022-Mar-22 at 13:36

I installed torchvision0.12.0, python3.8 and my OS is Windows. I succeeded in importing torch, but I couldn't import torchvision and getting this error.

ImportError

DLL load failed while importing _imaging: File "C:\Users'MyName'\Documents\GitHub\pytorch-cifar\main.py", line 8, in import torchvision

Is there someone who can solve this problem?

...

ANSWER

Answered 2022-Mar-22 at 13:36

This may be due to incompatible versions of torch and torchvision.You can get the information you want through the following link:

the corresponding torchvision versions and supported Python versions

Source https://stackoverflow.com/questions/71554974

QUESTION

Batch size reduces accuracy of ensemble of pretrained CNNs

Asked 2021-Aug-19 at 18:39

I'm trying to implement basic softmax-based voting, which I take a couple of pretrained CNNs, softmax their outputs, add them together and then use argmax as final output.

So I loaded 4 different pretrained CNNs (vgg11, vgg13, vgg16, vgg19) from "chenyaofo/pytorch-cifar-models" that were trained on CIFAR10 -- I didn't train them.

When I iterate over the test set with DataLoader with batch_size=128/256, I get to 94% accuracy;
When I iterate over the test set with batch_size=1, I get to 69% accuracy.

How could it be?

This is the code:

...

ANSWER

Answered 2021-Aug-19 at 15:17

You're forgetting to call model.eval():

Source https://stackoverflow.com/questions/68849347

QUESTION

IndexError: list index out of range in prediction of images

Asked 2021-Jul-12 at 02:13

I am doing predictions on images where I write all classes' names and in the test folder, I have 20 images. Please give me some hint as, why I am getting error? How we can check the indices of the model?

Code

...

ANSWER

Answered 2021-Jul-12 at 02:13

Your error stems from the fact that you don't do any modification to the linear layers of your resnet model.

I suggest adding this code:

Source https://stackoverflow.com/questions/68340882

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

Vulnerabilities

No vulnerabilities reported

Install pytorch-cifar

You can download it from GitHub.
You can use pytorch-cifar 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 .

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