Are Open-sourced Implementations Sometimes Over-engineered?

• glasses

  2 413 1.8 Jupyter Notebook

  High-quality Neural Networks for Computer Vision 😎

Yes, they are. Take with a grain of salt, but researchers (usually) do not know how to code and (or) they don't care to properly share their work. Things that are learned in the first Computer Science bachelor year, like OOP, DRY, packages, good variables/function naming, are apparently not used in ml research. This is why I created my own library (https://github.com/FrancescoSaverioZuppichini/glasses), for me, good code means less time I have to spend working and more free time.

• yolov5

  129 46,921 8.8 Python

  YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

  

Try YOLOv5. It's super easy to select a model and train on custom data: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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