Search Results for "object recognition"

Darknet is an open source neural network framework written in C and CUDA, developed by Joseph Redmon. It is best known as the original implementation of the YOLO (You Only Look Once) real-time object detection system. Darknet is lightweight, fast, and easy to compile, making it suitable for research and production use. The repository provides pre-trained models, configuration files, and tools for training custom object detection models. With GPU acceleration via CUDA and OpenCV integration, it achieves high performance in image recognition tasks. ...

MultiPathNet is a Torch-7 implementation of the “A MultiPath Network for Object Detection” paper (BMVC 2016), developed by Facebook AI Research. It extends the Fast R-CNN framework by introducing multiple network “paths” to enhance feature extraction and object recognition robustness. The MultiPath architecture incorporates skip connections and multi-scale processing to capture both fine-grained details and high-level context within a single detection pipeline. ...

The software resents an executable specification language based on deterministic finite automata, this allows the user to perform tests of word recognition in the state machines that are defined by it. Moreover, it’s possible for the user to assign snippets of code to be executed every transition automaton, these snippets can be described in Lua or PHP programming language. To test that were conducted in the language, was developed a compiler that recognizes words and generates object code.