This blog post was originally published at Quadric’s website. It is reprinted here with the permission of Quadric. The term “model zoo” first gained prominence in the world of AI / machine learning beginning in the 2016-2017 timeframe. Originally used to describe open-source public repositories of working AI models – the most prominent of which today […]
Let’s Visit the Zoo Read More +
This blog post was originally published at ENERZAi’ website. It is reprinted here with the permission of ENERZAi. Today, we’ll share results where we re-trained the original Whisper for optimal Korean ASR(Automatic Speech Recognition), applied Post-Training Quantization (PTQ), and provided a richer Pareto analysis so customers with different constraints and requirements can pick exactly what
Small Models, Big Heat — Conquering Korean ASR with Low-bit Whisper Read More +
Nov 18, 2025 Geneva, Switzerland — STMicroelectronics (NYSE: STM), a global semiconductor leader serving customers across the spectrum of electronics applications, has unveiled new models and enhanced project support for its STM32 AI Model Zoo to accelerate the prototyping and development of embedded AI applications. This marks a significant expansion for what is already the industry’s largest
Quality training data – especially segmented visual data – is a cornerstone of building robust vision models. Meta’s recently announced Segment Anything Model 3 (SAM3) arrives as a potential game-changer in this domain. SAM3 is a unified model that can detect, segment, and even track objects in images and videos using both text and visual
SAM3: A New Era for Open‑Vocabulary Segmentation and Edge AI Read More +
This blog post was originally published at Arm’s website. It is reprinted here with the permission of Arm. Discover how SME2 brings flexible, high-performance AI denoising to mobile photography for sharper, cleaner low-light images. Every smartphone photographer has seen it. Images that look sharp in daylight but fall apart in dim lighting. This happens because
This blog post was originally published at Plainsight Technologies’ website. It is reprinted here with the permission of Plainsight Technologies. A computer vision model that works under one lighting condition, store layout, or camera angle can quickly fail as conditions change. In the real world, nothing is constant, seasons change, lighting shifts, new objects appear and
The Need for Continuous Training in Computer Vision Models Read More +
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. ExecuTorch 1.0, an open source solution to training and inference on the Edge, becomes available to all developers Qualcomm Technologies contributed the ExecuTorch repository for developers to access Qualcomm® Hexagon™ NPU directly This streamlines the developer workflow
Bringing Edge AI Performance to PyTorch Developers with ExecuTorch 1.0 Read More +
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. At the ROSCon robotics conference, NVIDIA announced contributions to the ROS 2 robotics framework and the Open Source Robotics Alliance’s new Physical AI Special Interest Group, as well as the latest release of NVIDIA Isaac ROS. This
NVIDIA Contributes to Open Frameworks for Next-generation Robotics Development Read More +
This blog post was originally published at BrainChip’s website. It is reprinted here with the permission of BrainChip. Accelerating LiDAR Point Cloud with BrainChip’s Akida™ PointNet++ Model. LiDAR (Light Detection and Ranging) technology is the key enabler for advanced Spatial AI—the ability of a machine to understand and interact with the physical world in three
Unleash Real-time LiDAR Intelligence with BrainChip Akida On-chip AI Read More +
This blog post was originally published at Nota AI’s website. It is reprinted here with the permission of Nota AI. With the introduction of NVFP4—a new 4-bit floating point data type in NVIDIA’s Blackwell GPU architecture—LLM inference achieves markedly improved efficiency. Blackwell’s NVFP4 format (RTX PRO 6000) delivers up to 2× higher LLM inference efficiency
NVIDIA Blackwell: The Impact of NVFP4 For LLM Inference Read More +
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