Which is better OpenCV or TensorFlow for object detection?

To build Yolo we’re going to need Tensorflow (deep learning), NumPy (numerical computation) and Pillow (image processing) libraries . Also we’re going to use seaborn’s color palette for bounding boxes colors.

To build Yolo we’re going to need Tensorflow (deep learning), NumPy (numerical computation) and Pillow (image processing) libraries . Also we’re going to use seaborn’s color palette for bounding boxes colors.

Conclusion. To summarize: Tensorflow is better than OpenCV for some use cases and OpenCV is better than Tensorflow in some other use cases . Tensorflow’s points of strength are in the training side.

Conclusion. To summarize: Tensorflow is better than OpenCV for some use cases and OpenCV is better than Tensorflow in some other use cases . Tensorflow’s points of strength are in the training side.

The best real-time object detection algorithm (Accuracy) On the MS COCO dataset and based on the Mean Average Precision (MAP), the best real-time object detection algorithm in 2021 is YOLOR (MAP 56.1) . The algorithm is closely followed by YOLOv4 (MAP 55.4) and EfficientDet (MAP 55.1).

OpenCV is a library of programming functions mainly aimed at real-time computer vision. TensorFlow is an open-source software library for dataflow programming across a range of tasks. The important difference is that TensorFlow is a framework for machine learning, and OpenCV is a library for computer vision .

OpenCV is a library of programming functions mainly aimed at real-time computer vision. TensorFlow is an open-source software library for dataflow programming across a range of tasks. The important difference is that TensorFlow is a framework for machine learning, and OpenCV is a library for computer vision .