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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Lightly vs ML Kit

Lightly vs ML Kit

OverviewComparisonAlternatives

Overview

ML Kit
ML Kit
Stacks137
Followers209
Votes0
Lightly
Lightly
Stacks0
Followers1
Votes0
GitHub Stars3.6K
Forks313

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Detailed Comparison

ML Kit
ML Kit
Lightly
Lightly

ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.

Data labeling is time-consuming and can be very expensive. Lightly tells companies which data to label to have the biggest impact on model accuracy while saving time and costs.

Image labeling - Identify objects, locations, activities, animal species, products, and more; Text recognition (OCR) - Recognize and extract text from images; Face detection - Detect faces and facial landmarks; Barcode scanning - Scan and process barcodes; Landmark detection - Identify popular landmarks in an image; Smart reply - Provide suggested text snippet that fits context
Save money on your data related costs by removing redundancies; Reduce overfitting and improve generalization by diversifying your dataset
Statistics
GitHub Stars
-
GitHub Stars
3.6K
GitHub Forks
-
GitHub Forks
313
Stacks
137
Stacks
0
Followers
209
Followers
1
Votes
0
Votes
0
Integrations
No integrations available
Linux
Linux
Python
Python
Mac OS X
Mac OS X

What are some alternatives to ML Kit, Lightly?

Cloudinary

Cloudinary

Cloudinary is a cloud-based service that streamlines websites and mobile applications' entire image and video management needs - uploads, storage, administration, manipulations, and delivery.

imgix

imgix

imgix is the leading platform for end-to-end visual media processing. With robust APIs, SDKs, and integrations, imgix empowers developers to optimize, transform, manage, and deliver images and videos at scale through simple URL parameters.

TensorFlow

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.

OpenCV

OpenCV

OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.

scikit-learn

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

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.

ImageKit

ImageKit

ImageKit offers a real-time URL-based API for image & video optimization, streaming, and 50+ transformations to deliver perfect visual experiences on websites and apps. It also comes integrated with a Digital Asset Management solution.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

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

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

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