Fritz vs baikal
Overview
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CLI (Node.js)
Manual
Detailed Comparison
Fritz is the end-to-end solution for on-device ML. Create ML-powered features in your app with ease and cross-platform support. | It is a graph-based, functional API for building complex machine learning pipelines of objects that implement the scikit-learn API. It is mostly inspired on the excellent Keras API for Deep Learning, and borrows a few concepts from the TensorFlow framework and the (perhaps lesser known) graphkit package. It aims to provide an API that allows to build complex, non-linear machine learning pipelines. |
Image labeling - With the Image Labeling feature, you can identify the contents of an image or each frame of live video. Each prediction returns a set of labels as well as a confidence score for each label. Image Labeling can recognize people, places, and things. The underlying ML model was trained on millions of images and hundreds of labels.; Object detection - With the Object Detection feature, you can identify objects of interest in an image or each frame of live video. Each prediction returns a set of objects, each with a label, bounding box, and confidence score. | Build non-linear pipelines effortlessly;
Handle multiple inputs and outputs;
Add steps that operate on targets as part of the pipeline;
Nest pipelines;
Use prediction probabilities (or any other kind of output) as inputs to other steps in the pipeline;
Query intermediate outputs, easing debugging;
Freeze steps that do not require fitting;
Define and add custom steps easily;
Plot pipelines |
Statistics | |
GitHub Stars - | GitHub Stars 590 |
GitHub Forks - | GitHub Forks 30 |
Stacks 0 | Stacks 4 |
Followers 4 | Followers 11 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to Fritz, baikal?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
TensorFlow.js
Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
Polyaxon
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
Streamlit
It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
H2O
H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
