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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LLMGoogle's new native any-to-any AI model effectively collapses the traditional generative stack. Explore the architectural shift from stitched pipelines to unified latent spaces and what it means for developers.
Deep LearningByteDance's new Lance model is revolutionizing AI by unifying image understanding, generation, and editing into a single dual-stream architecture. Discover how collaborative multi-task training is ending the era of fragmented machine learning pipelines.
Deep LearningDive deep into the architecture of LatentSync and learn how to build a production-ready lip-syncing pipeline. We explore audio conditioning, temporal consistency, and end-to-end implementation using Latent Diffusion Models.
LLMGoogle's mid-2026 release of Gemini 3.5 Flash marks a turning point for autonomous AI development. By drastically lowering inference costs while mastering complex agentic coding tasks, it paves the way for scalable multi-agent systems.
LLMDiscover how to leverage the newly released 9-billion parameter Qwopus3.5-9B-Coder model for local agentic workflows. We build an end-to-end autonomous coding assistant capable of complex tool calling entirely on consumer hardware.
Machine LearningWhile AI models like GraphCast forecast global weather 10,000 times faster than traditional methods, a recent study reveals their critical vulnerability. Discover why physics-based models remain the undisputed champions of predicting unprecedented, record-shattering climate events.
Deep LearningTsinghua University researchers recently unveiled Causal Forcing++, a novel diffusion distillation technique. This breakthrough enables high-fidelity autoregressive video generation in real time by drastically reducing inference steps without sacrificing temporal consistency.
Deep LearningMeta has released LagerNVS on Hugging Face, introducing a novel encoder-decoder architecture that bypasses explicit 3D reconstruction. By leveraging latent geometry, it achieves real-time, state-of-the-art novel view synthesis from single images and pairs seamlessly with diffusion models for generative extrapolation.
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.
