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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LLMAnthropic's latest model introduces variable thinking budgets and flawless tool execution. Discover how Claude Fable 5 redefines human-in-the-loop workflows and complex software engineering tasks.
Deep LearningMicrosoft Research recently unveiled Mirage, a breakthrough framework that bypasses pixel-space reconstruction to store 3D scene data directly in diffusion latents. This approach drops memory usage by a factor of 55 while delivering unprecedented generation speeds.
Machine LearningOpenEnv has officially transitioned to a multi-organization governance model backed by Meta, Hugging Face, and Nvidia to standardize agentic AI environments. This critical infrastructure shift provides open-source developers with the standardized tooling needed to close the reinforcement learning gap with proprietary frontier models.
LLMMistral AI's latest 119-billion parameter model upends our understanding of the open-source ecosystem. We analyze its architecture, hardware realities, and the practical strategies for serving a massive open-weights model in production.
PythonDiscover how to implement long-term context retention and autonomous knowledge-building in your AI agents without relying on expensive proprietary APIs. This comprehensive guide walks you through building a stateful, memory-aware assistant using the trending open-source Python library MemPalace.
Deep LearningGoogle's Magenta team has released RealTime 2, a 2.4-billion parameter open-weights model reshaping interactive music generation. Dive into the architecture and Python library enabling sub-millisecond audio synthesis driven by MIDI, text, and audio cues.
Deep LearningDiscover how the groundbreaking SimLBR model flips the script on synthetic media detection. By abandoning binary classification, it learns to identify deepfakes simply by mastering the bounded manifold of authentic photography.
Deep LearningAttenFusion-Net integrates ResNet architectures with dual-attention mechanisms to solve the hardest challenges in automated waste sorting. Learn how this framework uses spatial and channel attention to bypass background noise and accurately classify deformed municipal waste.
LLMA groundbreaking new approach inspired by biological neuroscience introduces a dedicated 'sleep' phase for Large Language Models. By allowing AI to offline-process and consolidate daily experiences, models can continuously learn and self-modify without suffering from catastrophic forgetting.
