Darknet vs. JAX vs. OpenFaceTracker vs. Python Comparison Chart

Darknet is an open-source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. You can find the source on GitHub or you can read more about what Darknet can do. Darknet is easy to install with only two optional dependencies, OpenCV if you want a wider variety of supported image types, and CUDA if you want GPU computation. Darknet on the CPU is fast but it's like 500 times faster on GPU! You'll have to have an Nvidia GPU and you'll have to install CUDA. By default, Darknet uses stb_image.h for image loading. If you want more support for weird formats (like CMYK jpegs, thanks Obama) you can use OpenCV instead! OpenCV also allows you to view images and detections without having to save them to disk. Classify images with popular models like ResNet and ResNeXt. Recurrent neural networks are all the rage for time-series data and NLP.

OpenFaceTracker is a facial recognition program capable to detect one or several faces on a picture or a video, and to identify them via a database. OpenFaceTracker needs OpenCV3.2 and QT4 installed on your machine, you’ve got two options, if you love compiling libraries by hand, please follow build_oft, and installing Opencv and QT using your favorite packaging tool. You can compile OFT as a library or you can compile it as a standalone binary file. You can then open the file and execute the detection and recognition module. You can show help and exit, show the list of all available cameras, you can test the XML DB, read from the OFT config, and check the environment. OpenFaceTrackerLib uses Opencv 3.2. This latter has introduced many new algorithms and features comparing to version 2.4. Some modules have been rewritten, some have been reorganized. Although most of the algorithms from 2.4 are still present, the interfaces can differ.