YOLO TensorFlow
TensorFlow implementation of YOLO for real-time object detection using pretrained YOLO_small, YOLO_tiny, and YOLO_face models.
What is YOLO TensorFlow?
YOLO_tensorflow is a TensorFlow implementation of the YOLO (You Only Look Once) real-time object detection algorithm. It allows developers to perform fast object detection on images using pretrained YOLO models converted from Darknet. The project focuses on inference only, providing an easy way to integrate YOLO into Python-based computer vision applications without training overhead.
Developers and researchers working on computer vision projects who want to use YOLO for real-time object detection within the TensorFlow ecosystem, especially those needing a simple inference pipeline.
It offers a clean, inference-only TensorFlow port of YOLO with support for multiple pretrained models, making it easier to deploy YOLO in Python environments compared to the original C-based Darknet implementation.
Overview
tensorflow implementation of 'YOLO : Real-Time Object Detection'
Use Cases
Best For
- Adding real-time object detection to Python applications
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