Ultralytics
Ultralytics offers a full-stack vision-AI platform built around its flagship YOLO model suite that enables teams to train, validate, and deploy computer-vision models with minimal friction. The platform allows you to drag and drop datasets, select from pre-built templates or fine-tune custom models, then export to a wide variety of formats for cloud, edge or mobile deployment. With support for tasks including object detection, instance segmentation, image classification, pose estimation and oriented bounding-box detection, Ultralytics’ models deliver high accuracy and efficiency and are optimized for both embedded devices and large-scale inference. The product also includes Ultralytics HUB, a web-based tool where users can upload their images/videos, train models online, preview results (even on a phone), collaborate with team members, and deploy via an inference API.
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Google Cloud Vision AI
Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more. Google Cloud offers two computer vision products that use machine learning to help you understand your images with industry-leading prediction accuracy. Automate the training of your own custom machine learning models. Simply upload images and train custom image models with AutoML Vision’s easy-to-use graphical interface; optimize your models for accuracy, latency, and size; and export them to your application in the cloud, or to an array of devices at the edge. Google Cloud’s Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. Assign labels to images and quickly classify them into millions of predefined categories. Detect objects and faces, read printed and handwritten text, and build valuable metadata into your image catalog.
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V7 Darwin
V7 Darwin is a powerful AI-driven platform for labeling and training data that streamlines the process of annotating images, videos, and other data types. By using AI-assisted tools, V7 Darwin enables faster, more accurate labeling for a variety of use cases such as machine learning model training, object detection, and medical imaging. The platform supports multiple types of annotations, including keypoints, bounding boxes, and segmentation masks. It integrates with various workflows through APIs, SDKs, and custom integrations, making it an ideal solution for businesses seeking high-quality data for their AI projects.
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RectLabel
An offline image annotation tool for object detection and segmentation. Draw polygons, cubic bezier curves, line segments, and points. Draw oriented bounding boxes in aerial images. Draw key points with a skeleton. Draw pixels with brushes and superpixels. Read/write in PASCAL VOC xml and YOLO text formats. Export to CreateML object detection and image classification formats. Export to COCO, Labelme, YOLO, DOTA, and CSV formats. Export indexed color mask images and grayscale mask images. Settings for objects, attributes, hotkeys, and labeling fast. Customize the label dialog to combine with attributes. 1-click buttons speed up selecting the object name. Auto-suggest works for more than 5000 object names. Search object, attribute, and image names in a gallery view. Automatic labeling using Core ML models. Automatic text recognition using OCR. Video to image frames, augment images, etc. Supports English, Chinese, Korean, and 11 other languages.
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