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awesome-experimental-standards-deep-learning
Repository collecting resources and best practices to improve experimental rigour in deep learning research.
Very neat! I will add this to https://github.com/Kaleidophon/experimental-standards-deep-learning-research :-)
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Nice to see a python implementation of deLong's method - I've had to use pROC (in R) for that in the past. For binary event analysis (among other things) there's also https://github.com/drsteve/PyForecastTools, which also has bootstrapped confidence intervals, or analytic CI using Wald or Agresti-Coull. The terminology is from weather literature, but it covers a lot of the same ground.
Related posts
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Anybody building ML models in C++?
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[D] Fixing the angle of Skewed Paintings, see comments
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How do I train YOLO5 to detect small objects (arial imagery). something like 20-20 pixels or maybe little more? How do I increase resolution and apply augmentation and tiling? Or maybe the YOLO5 is not he best choice for that?
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I just realized yolov5 is GPL-3
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I was excited about YOLOv7, so I built a sharable object detection application with VDP and Streamlit.