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Reminds me of Machine Box (http://machinebox.io). Source: over 1 year ago
Thank you :) I did that to teach dog’s breed to an AI. If you don’t know machine box yet : Https://machinebox.io It seems really cool and easy to use. Source: almost 2 years ago
I think you should go 5 Pi X 5 Jetson Nano’s I haven’t seen many people offloading the Nano’s GPU functionality for ML similar to this Serverless style of product. https://machinebox.io/. Source: over 2 years ago
For face recognition - CompreFace. Disclaimer - I created it, as an alternative you can use MachineBox, but it's not open source and has limits. Also, I think, you will use some software to control the system, e.g. Frigate or Home Assistant, I think this repository can be useful for you. Source: over 2 years ago
If you have a really simple application, you can just save the encodings into the files. If not - it's better to use a database. SQL is ok. But for the best results, I would suggest using milvus.io, as it was created for saving vectors and finding the distances (I haven't tried it, though). If your final goal is not to learn face recognition basics, you can just use free ready to use solutions like CompreFace... Source: almost 3 years ago
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 5 months ago
You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/). Source: 5 months ago
- [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection. Source: 9 months ago
I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: "My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…" / X ) made by Mckay Wrigley and I was wondering what's the easiest... Source: 9 months ago
You also need C++ if you're going to do things which aren't built in as part of the engine. As an example if you're looking at using compute shaders, inbuilt native APIs such as a mobile phone's location services, or a third-party library such as OpenCV, then you're going to need C++. Source: 11 months ago
Amazon Machine Learning - Machine learning made easy for developers of any skill level
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Model Zoo - Deploy your machine learning model in a single line of code.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
MorphL - Applied AI/ML for eCommerce
NumPy - NumPy is the fundamental package for scientific computing with Python