Based on our record, Amazon Rekognition seems to be a lot more popular than Darknet. While we know about 33 links to Amazon Rekognition, we've tracked only 3 mentions of Darknet. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
This reminds me of the resume for the guy who made darknet Https://pjreddie.com/darknet/. Source: over 1 year ago
Election of tools: you should define if you are going to use machine/deep learning methods or classical approaches such as the Viola-Jones algorithm. I will recommend you to use ML/DL with TensorFlow (Object Detection API) or Darknet (YOLO). Source: over 2 years ago
Yes, in subfield of ML like DNL and CNL, C||C++ are commonly used, darkent is open source neural network framework written in c and cuda . Source: almost 3 years ago
AWS Rekognition is a great choice for many types of real-world projects or just for testing an idea on your images. The issue eventually comes with its cost, unfortunately, which we will see later in a specific example. Don’t get me wrong, Rekognition is a great service and I love to use it for its simplicity and reliable performance on quite a few projects. - Source: dev.to / about 2 months ago
I don’t really want to spend so much time manually adjusting labels. For most machine learning, the next step would be to fine tune your model. You can essentially fine tune Amazon Rekognition by using Custom Labels. You can do this to make it better at detecting specific objects (like bears) or train it to detect new objects like your product or logo. It really depends on your application needs. - Source: dev.to / 10 months ago
For instance, are you a company with lots of security cameras? Hire me to write a program that pipes your data into AWS rekognition and then shows you a dashboard of what happened on your cams today. Got a ton of products with no meta-description? Hire me to write a program that pipes your data into OpenAI, and then saves the generated description to your custom CMS. Source: 11 months ago
Amazon Rekognition: Used to index, detect faces in the picture, and compare faces when users try voting, it was the heart of the facial voting feature. - Source: dev.to / about 1 year ago
Sure. But if you think generating thumbnails and detecting intros/credits takes a long time, wait until your computer is running machine learning/computer vision over your entire library. They also have to build and train that model which is no trivial task. And I know what you're thinking, why don't they just use Amazon's Rekognition service that does celebrity identification? Well, it's $0.10 per minute of... Source: about 1 year ago
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Google Vision AI - Cloud Vision API provides a comprehensive set of capabilities including object detection, ocr, explicit content, face, logo, and landmark detection.
TFlearn - TFlearn is a modular and transparent deep learning library built on top of Tensorflow.
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TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
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