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.

So let’s go over the steps that our main function will have to perform to successfully run the application.

So let’s go over the steps that our main function will have to perform to successfully run the application.

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).