Face and basic landmarks detection using mediapipe models with efficiency and very good accuracy and draw on image or save detected faces using opencv in python.
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Deep LearningA groundbreaking PNAS paper demonstrates an optical processing unit training massive neural networks at 1,500 TeraOPS using under 30 watts of power. By combining photonics with direct feedback alignment, this hybrid architecture shatters the energy limitations of traditional GPU backpropagation.
Deep LearningNVIDIA has released SANA-WM, an open-source 2.6-billion-parameter world model capable of generating minute-scale videos and simulated environments. This milestone democratizes physical world simulation for generative AI and autonomous agent training.
Deep LearningMicrosoft briefly uploaded and deleted two unannounced image generation models, Lens and Lens-Turbo, on Hugging Face. The sudden appearance has the AI community buzzing about the company's next steps in open-weight computer vision.
Deep LearningA new preprint from StentorLabs challenges the foundational assumption that deeper neural networks are inherently better. We explore the depth ceiling effect and why widening your transformer often yields superior performance.
LLMMicrosoft's new DELEGATE-52 benchmark reveals that even the most advanced frontier models struggle with long-running, multistep workflows. We break down why agents corrupt documents, lose context, and how developers can build more resilient systems.
Deep LearningNVIDIA's SANA-WM leverages a hybrid linear diffusion transformer to bypass the quadratic compute bottlenecks of traditional video generation. This architectural breakthrough enables highly efficient, minute-scale world modeling with profound implications for robotics, autonomous driving, and real-time simulation.
Deep LearningDiscover how the open-source 9B parameter Sulphur 2 Base model is revolutionizing local video generation. We explore its architecture, ComfyUI integration, and provide a comprehensive guide to running cinematic text-to-video workloads on your own hardware.
PythonTransformers 5.0 revolutionizes local AI inference by integrating out-of-the-box Mixture of Experts routing and a native memory offloading engine. These powerful architectural upgrades finally allow developers to run massive models on standard consumer hardware without catastrophic memory limitations.
LLMMicrosoft recently unveiled MDASH, a multi-model agentic system that uses over 100 specialized AI agents to autonomously discover and validate complex software vulnerabilities. By utilizing a collaborative pipeline of auditor, debater, and prover agents, this system goes beyond traditional scanners to uncover deeply hidden flaws.
