fvcore | common code that 's shared among different research projects | Computer Vision library

by facebookresearch Python Version: 0.1.dev200506 License: Apache-2.0

X-Ray Key Features Code Snippets(4)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | fvcore Summary

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

fvcore is a light-weight core library that provides the most common and essential functionality shared in various computer vision frameworks developed in FAIR, such as Detectron2, PySlowFast, and ClassyVision. All components in this library are type-annotated, tested, and benchmarked. The computer vision team in FAIR is responsible for maintaining this library.

Support

Quality

Security

License

Reuse

Support

fvcore has a medium active ecosystem.

It has 1638 star(s) with 208 fork(s). There are 34 watchers for this library.

It had no major release in the last 12 months.

There are 23 open issues and 43 have been closed. On average issues are closed in 34 days. There are 11 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of fvcore is 0.1.dev200506

Quality

fvcore has 1 bugs (1 blocker, 0 critical, 0 major, 0 minor) and 44 code smells.

Security

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

fvcore code analysis shows 0 unresolved vulnerabilities.

There are 2 security hotspots that need review.

License

fvcore 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

fvcore releases are not available. You will need to build from source code and install.

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.

fvcore saves you 2255 person hours of effort in developing the same functionality from scratch.

It has 4930 lines of code, 409 functions and 53 files.

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

Top functions reviewed by kandi - BETA

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

Takes a list of polygons
Apply coordinates to coordinates
Evaluate the einsum operator
Returns the shape of a tensor
Apply the bounding box
Applies coordinates to the given coordinates
Return a function that calculates the number of activations for a given operation
Get the package version
Returns a function that returns an element - wise elementwise
Resume or load a checkpoint file
Load a checkpoint from scratch
Returns the path to the checkpoint file
Check if the last checkpoint exists
Generates a counter function for normalization operations
Compute the number of convolution matrices
Compute the number of conv op
Apply coordinates to a list of polygons
Calculate the number of nn
Apply a segmentation
Apply the given image
Implements BMM flop
Linear interpolation
Apply segmentation
Matrix multiplication op
Batch normalization op
Generate a counter function for norm layers
Get the aliases for the given model

Get all kandi verified functions for this library.

fvcore Key Features

No Key Features are available at this moment for fvcore.

fvcore Examples and Code Snippets

EMANet,Requirements

Python

Lines of Code : 8

License : No License

Copy

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

pip install opencv-python

pip install tensorboard

pip install git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI

pip install git+https://github.com/facebookresearch/

Facebook's Detectron2_Demo_Windows_10,Configuration and Installation steps

Python

Lines of Code : 7

License : No License

Copy

pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI

pip install -r requirement.txt

git clone https://github.com/facebookresearch/detectron2.git
cd detectron2 && pip install -e .

git clone https://github.com/f

DANet,Requirements

Python

Lines of Code : 7

License : No License

Copy

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

pip install opencv-python

pip install tensorboard

pip install git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI

pip install git+https://github.com/facebookresearch/

Cython_bbox and lap installation error, #include "Python.h" not found

Python

Lines of Code : 2

License : Strong Copyleft (CC BY-SA 4.0)

Copy

sudo apt install libpython3.9-dev

Community Discussions

Trending Discussions on fvcore

Docker container random segmentation fault

Cuda version issue while using Detectron2 in Google Colab

QUESTION

Docker container random segmentation fault

Asked 2020-Jul-07 at 21:01

I am trying to run an application on a Docker container, but the program is randomly generating segmentation faults. Sometimes the code runs as it is supposed to. Other times, when I interrupt its execution (Ctrl + C) and run it again, it segfaults.

Below is my Dockerfile and the output from gdb. I can see that the problem boils down to cv2.VideoCapture, but I already tried a few fixes (like locales) and it didn't work. On the host machine (i.e., outside the container) the code runs fine. Any help would be greatly appreciated.

Dockerfile:

...

ANSWER

Answered 2020-Jul-07 at 21:01

To anyone who might come across this problem, use the headless version of opencv pip install opencv-python-headless

This is what finally fixed the random segmentation fault problem.

Source https://stackoverflow.com/questions/62776697

QUESTION

Cuda version issue while using Detectron2 in Google Colab

Asked 2020-Jun-12 at 10:40

I am trying to run the Detectron2 module on Colab using CUDA version 10.0 but since today there have been some issues regarding the versions of Cuda Compiler.

The output I get after running !nvidia-smi is :

...

ANSWER

Answered 2020-Jun-12 at 10:39

The problem was with the compiled Detectron2 Cuda runtime version and once I recompiled Detectron2 the error was solved.

Here is the result from !python -m detectron2.utils.collect_env command:

Source https://stackoverflow.com/questions/62341457

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

Vulnerabilities

No vulnerabilities reported

Install fvcore

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