Zero-shot learning 4

Unified model for segmenting objects across images and videos with high precision. Meta Segment Anything Model 2 (SAM 2) is the first unified model for segmenting objects across images and videos. It allows users to select objects in any image or video frame using a click, box, or mask as input. SAM 2 is designed for fast, precise object selection and offers state-of-the-art performance for object segmentation in both images and videos. The models are open source under an Apache 2.0 license.

AI-assisted data labeling tool for fast object detection and dataset creation. T-Rex Label is an AI-assisted data labeling tool that allows users to automatically label similar objects by selecting one as a visual prompt. It aims to save 99% of labeling time and requires no installation or fine-tuning. It supports COCO and YOLO dataset labeling and can integrate with platforms like Roboflow and Labelbox for AI data training.

SAM is a promptable AI segmentation system for zero-shot generalization to objects and images. Segment Anything (SAM) is a promptable segmentation system developed by Meta AI that offers zero-shot generalization to unfamiliar objects and images without requiring additional training. It allows users to "cut out" any object in any image with a single click. SAM uses a variety of input prompts to perform a wide range of segmentation tasks. The model was trained on millions of images and masks collected through a model-in-the-loop "data engine."

AI-powered voice cloning, text-to-speech, and speech-to-text platform. Voicv is a cutting-edge voice cloning platform that transforms your voice into a digital asset in minutes, supporting multiple languages and zero-shot learning. It offers advanced AI-powered voice cloning, text-to-speech (TTS), and speech-to-text (ASR) services. Users can create, transform, and convert audio with cutting-edge technology, supporting multiple languages and emotions.