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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 #99 · 07/14/2016, 04:57 AM

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https://github.com/daleroberts/tv tv ("#textview") is a small tool to quickly view high-resolution multi-band imagery directly in your terminal. It was designed for working with (very large) #satellite imagery data over a low-bandwidth connection. For example, you can directly visualise a Himawari 8 (11K x 11K pixel) image of the Earth directly from its URL: It is built upon the wonderful #GDAL library so it is able to load a large variety of image formats (GeoTiff, PNG, Jpeg, NetCDF, ...) and subsample the image as it reads from disk so it can handle very large files quickly. It has the ability to read filenames (or URLs) from stdin and load files directly from URLs without writing locally to disk. Command line options are styled after gdal_translate such as: -b to specify the bands (and ordering) to use, -srcwin xoff yoff xsize ysize to view a subset of the image, -r to specify the subsampling algorithm (nearest, bilinear, cubic, cubicspline, lanczos, average, mode).

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