Lambda Face Recognition API
Amazon Rekognition
PixLab
OpenFace
Kairos
Sightcorp
Affectiva
Google Vision AI
OpenCV
Scikit-learn
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NumPy
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How does this compare to https://lambdalabs.com/. - Source: Hacker News / 9 months ago
Another option is to pay for AWS server with a beefy GPU and enough RAM. It's not too cheap, but isn't expensive either if you aren't planning to run it 24/7. Or get a GPU cluster from a company that offers stuff for ML specifically, it might be easier to set up compared to AWS and in some cases cheaper. Like, for example, lambdalabs that offers H100 gpu for 2 bucks per hour. Source: 10 months ago
I used some of the cloud GPUs on Vast.ai, but I also tried Lambda Labs, and these days I have my own docker container setup which can be deployed to a VM on Google Cloud and used more programatically. Source: 12 months ago
Lambda | Full-Time | Software Engineers | Remote US & Canada | https://lambdalabs.com/ We are looking for talented software engineers to join our team. We're currently hiring for multiple engineering positions and more. Lambda is a fast growing startup providing deep learning hardware, software, and cloud services to the world's leading companies and research institutions. Lambda’s mission is to create a world... - Source: Hacker News / about 1 year ago
LambdaLabs has been good to me so far. Cheap pricing, easy spin up, and no bullshit about applying to use a GPU. Source: about 1 year 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: 12 months ago
Amazon Rekognition - Add Amazon's advanced image analysis to your applications.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
PixLab - PixLab is a machine learning SaaS platform which offer computer vision and media processing APIs.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
OpenFace - OpenFace is an open source face recognition solution with deep neural networks.
NumPy - NumPy is the fundamental package for scientific computing with Python