Humans can spot things super quick – all they need is just a glance. Computer scientists are teaching computers to do the same through object detection, classification and image recognition in AI. They’re getting machines to look at pictures or videos, figure out what’s in them, and slap labels on the details.
New paradigms of image recognition in AI are being explored since real-world use cases are on the rise. So here are six tools to help you build better computer vision AI.
YOLO
YOLO, short for ‘You Only Look Once’, is a widely adopted real-time object detection algorithm in computer vision, embraced by major tech players in commercial products. Introduced in 2016, the original model revolutionised object detection by outpacing its counterparts in speed.
Since then, various iterations, including YOLOv4, have emerged, each enhancing performance and efficiency. YOLOv7, unveiled in July 2022 by Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao, stands out as one of the fastest and most accurate real-time object detection models.
Notably, crafted by Ultralytics, YOLOv8 prioritises speed, accuracy, and user-friendliness, making it a top choice for tasks like object detection, tracking, instance segmentation, image classification, and pose estimation.
With innovations like Mosaic data enhancement, self-adversarial training, and cross-mini-batch normalisation, these YOLO iterations continue to advance the capabilities of computer vision systems.
ImageAI
ImageAI is an open-source Python library built to empower developers to build applications and systems with self-contained capabilities using simple and few lines of code.
Created by Moses Olafenwa, the library empowers programmers with all levels of expertise to easily integrate state-of-the-art computer vision features, train/deploy custom image/video AI models to detect and recognise custom objects.
The library has been installed over 400,000 times and has 7,000+ starts. Since 2018, Olafenwa has released more open source projects for AI inference and solving AI data problems with plans to build and release more to facilitate AI democratisation and access.
Some of the projects are IdenProf, FireNET, ActionNET, DeepStack_ExDark and TrafficNET.
PaddleClas
PaddleClas, developed by PaddlePaddle, is a robust image classification and recognition toolset, catering to both industry and academia within image recognition.
Tailored for training top-tier computer vision models, it supports diverse image classification models like those from ImageNet1k and PULC datasets, offering Python wheel packages for predictions. PaddleClas accommodates various network structures such as ResNet, MobileNet, and ShuffleNet with a range of documentation, including tutorials and application examples.
Its versatility extends to evaluation environments for both CPU and GPU, making it an invaluable resource for developers and researchers engaged in image classification and recognition endeavours.
Emgu CV
Emgu CV is a cross-platform .NET wrapper for the OpenCV image-processing library, facilitating the invocation of OpenCV functions from .NET compatible languages like C#, VB, VC++, and IronPython. Crafted entirely in C#, it seamlessly compiles in Mono, rendering compatibility across platforms supported by Mono—Windows, Linux, Mac OS X, iOS, and Android.
Boasting features like a generic image class, automatic garbage collection, XML serializable images, and Intellisense support, Emgu CV streamlines image-processing tasks. It supports generic pixel operations and arrives with illustrative code snippets. The current iteration is conveniently accessible as a NuGet package.
SOD Embedded
SOD was created to establish a unified foundation for computer vision applications, fostering the widespread adoption of machine perception in both open-source and commercial products.
This advanced, embedded, cross-platform computer vision and machine learning software library provides APIs for deep learning, sophisticated media analysis, and real-time, multi-class object detection.
Specifically designed for embedded systems with constrained computational resources and IoT devices, SOD encompasses a diverse array of classic and cutting-edge deep neural networks, complete with their pre-trained models. It is a versatile solution for accelerating machine perception across various applications and platforms.
MILVUS Bootcamp
This model is made to help with unstructured data like finding pictures, searching for audio or molecules, analysing videos, and working on questions and answers using natural language. It’s not a complete training program but has examples for developers and researchers to use with Milvus for different tasks.
The repository includes things that go along with Milvus Lite, a simpler version. You can find helpful examples and materials here if you’re trying to work on more straightforward Milvus-based solutions.
