android-camera2-preview-qr-code-scanner VS automl

Compare android-camera2-preview-qr-code-scanner vs automl and see what are their differences.

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android-camera2-preview-qr-code-scanner automl
3 5
8 5,105
- 0.9%
4.5 6.4
4 months ago 29 days ago
C++ Jupyter Notebook
- Apache License 2.0
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.

android-camera2-preview-qr-code-scanner

Posts with mentions or reviews of android-camera2-preview-qr-code-scanner. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-13.

automl

Posts with mentions or reviews of automl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-13.

What are some alternatives?

When comparing android-camera2-preview-qr-code-scanner and automl you can also consider the following projects:

mlkit - A collection of sample apps to demonstrate how to use Google's ML Kit APIs on Android and iOS

simple-faster-rcnn-pytorch - A simplified implemention of Faster R-CNN that replicate performance from origin paper

barcode-qrcode-images

TFLiteClassification - TensorFlow Lite Image Classification Python Implementation

SipMask - SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (ECCV2020)

SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data

FLAML - A fast library for AutoML and tuning.

H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

gpt-3 - GPT-3: Language Models are Few-Shot Learners