cv-combinators vs tensor-safe

cv-combinators

Functional Combinators for Computer Vision, currently using OpenCV as a backend (by sinelaw)

hackage.haskell.org

tensor-safe

A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras. (by leopiney)

AI Deep Learning haskell-library Dependent Types Functional programming Haskell Machine Learning

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cv-combinators		tensor-safe
	Project
-	Mentions	-
12	Stars	101
-	Growth	-
0.0	Activity	0.0
over 8 years ago	Latest Commit	over 1 year ago
Haskell	Language	Haskell
GNU General Public License v2.0 only	License	BSD 3-clause "New" or "Revised" License

The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

cv-combinators

Posts with mentions or reviews of cv-combinators. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning cv-combinators yet.
Tracking mentions began in Dec 2020.

tensor-safe

Posts with mentions or reviews of tensor-safe. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning tensor-safe yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing cv-combinators and tensor-safe you can also consider the following projects:

hnn - haskell neural network library

moo - Genetic algorithm library for Haskell. Binary and continuous (real-coded) GAs. Binary GAs: binary and Gray encoding; point mutation; one-point, two-point, and uniform crossover. Continuous GAs: Gaussian mutation; BLX-α, UNDX, and SBX crossover. Selection operators: roulette, tournament, and stochastic universal sampling (SUS); with optional niching, ranking, and scaling. Replacement strategies: generational with elitism and steady state. Constrained optimization: random constrained initialization, death penalty, constrained selection without a penalty function. Multi-objective optimization: NSGA-II and constrained NSGA-II.

HaVSA - HaVSA (Have-Saa) is a Haskell implementation of the Version Space Algebra Machine Learning technique described by Tessa Lau.

HSGEP - Haskell Gene Expression Programming Library

keera-posture - Alleviate your back pain using Haskell and a webcam

hasktorch - Tensors and neural networks in Haskell

GA - Haskell module for working with genetic algorithms

creatur - Framework for artificial life and other evolutionary algorithms.

opencv - Haskell binding to OpenCV-3.x

genprog - Genetic programming library

Etage - A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology.