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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Machine LearningHugging Face recently introduced ml-intern, an open-source AI agent built on the smolagents framework that autonomously handles LLM post-training workflows. This walkthrough explores how to leverage the repository to automate fine-tuning, debugging, and model evaluation.
LLMLarge Language Models are developing a fascinating habit of bypassing strict JSON constraints to autonomously invent custom UI elements. We explore the mechanics of this emergent behavior and its implications for human-computer interaction.
LLMNvidia recently released Nemotron 3 Super, a 120B-parameter open Mixture-of-Experts model that activates just 12.7B parameters per forward pass. We break down how its hybrid Mamba-Transformer backbone achieves unprecedented throughput and natively supports one-million token context windows for complex agentic AI.
LLMAlibaba's new 35-billion parameter MoE model activates only 3 billion parameters per pass, achieving 73.4% on SWE-Bench Verified. It brings enterprise-grade coding agents directly to consumer hardware.
Deep LearningRecent breakthroughs reveal that Large Language Models undergo structural phase transitions in their hidden activation spaces during complex reasoning. By mapping the spectral geometry of these token trajectories, we can now predict exact model correctness before the final output is generated.
Deep LearningHugging Face has integrated SAM-3 Lite-Text, utilizing knowledge distillation and a MobileCLIP student model to reduce the text encoder's parameters by 88%. This deep dive explores how this architecture works and provides a practical guide for deploying fast, text-prompted segmentation on edge devices.
LLMMeta has disrupted the AI ecosystem with Llama 4 Maverick, a 400-billion parameter Mixture of Experts model featuring an unprecedented 10-million-token context window. This deep dive explores the engineering breakthroughs behind the model and how it enables entire product architectures to be loaded in a single prompt.
Deep LearningUC Berkeley's fiwGAN and FinneGAN translate abstract latent spaces into interactive, physical 3D environments. This breakthrough not only solves critical AI explainability hurdles but also reveals how generative networks develop language through informative imitation.
Deep LearningHugging Face has integrated the SAM-3 Lite-Text model into the Transformers library, revolutionizing edge-deployed computer vision. By leveraging MobileCLIP and knowledge distillation, this release achieves an 88 percent parameter reduction while maintaining state-of-the-art segmentation performance.
