What is OpenFace and what are its top alternatives?
Top Alternatives to OpenFace
- 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. ...
- 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. ...
- Rekognition API
ReKognition API offers services for detecting, recognizing, tagging and searching faces and concepts as well as categorizing scenes in any photo, through a RESTFUL API. We process and analyze photos from anywhere, so you can mix and match photo sources with user IDs, which can enable you to, say, recognize objects in Facebook and Flickr photos. ...
- Kairos API
Commercial-grade emotion analysis, face detection and recognition engine provided as a public API. Kairos takes the complexity out of facial recognition and emotion analysis so you can focus on building a great product. ...
- FaceFusion
It is a next-generation face swapper and enhancer that uses artificial intelligence to create realistic and high-quality results. ...
OpenFace alternatives & related posts
- Computer Vision36
- Open Source17
- Imaging12
- Face Detection9
- Machine Learning9
- Great community6
- Realtime Image Processing4
- Helping almost CV problem2
- Image Augmentation2
related OpenCV posts
Hi Team,
Could you please suggest which one need to be used in between OpenCV and FFMPEG.
Thank you in Advance.
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
- Hard9
- Hard to debug6
- Documentation not very helpful2
related TensorFlow posts
Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.
Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.
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