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Source channel @githubtrending · Post #14949 · Jul 12

#jupyter_notebook#darknet#pytorch#scaled_yolov4#yolor#yolov3#yolov4#yolov7 YOLOv7 is a powerful tool for detecting objects in images and videos. It is fast, accurate, and can work well on devices with limited power, making it useful for real-time applications like self-driving cars and surveillance systems. YOLOv7 uses advanced techniques like Feature Pyramid Networks to detect objects of different sizes and can handle complex scenes with overlapping objects. This makes it beneficial for users who need quick and precise object detection in various environments. https://github.com/WongKinYiu/yolov7

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djangoproject

@djangoproject · Post #598 · 04/24/2018, 02:56 AM

https://github.com/Djeact/Djeact-RoadMap What is Djeact? The name of Djeact comes from the combination of Django and React. Django is a python web framework and React is a javascript library for building user interfaces. Djeact is a set of libraries which the backend is written by django and the UI is written with react. Following libraries have been written so far: Why #Djeact started? Why #Django was chosen for #backend? Why #React was chosen for #frontend? Why #MIT#License?