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.
Read more.
LLMMeta has officially abandoned its purely open-source playbook with the launch of Muse Spark. Achieving a staggering 86.4% on the CharXiv Reasoning benchmark, this proprietary API fundamentally redefines the frontier AI landscape.
Deep LearningTencent has open-sourced HY-Embodied-0.5, a groundbreaking foundation model suite designed specifically for real-world robotics. Discover how its novel Mixture-of-Transformers architecture and latent token routing are radically accelerating Vision-Language-Action pipelines.
Deep LearningMIT researchers have developed CompreSSM, a novel technique using classical control theory to dynamically compress state-space models during training. By calculating controllability and observability, this method sheds unnecessary complexity without sacrificing performance.
LLMMoonshot AI has disrupted the large language model landscape with Kimi K2.5, featuring a massive one-million-token context window. Discover how this model rivals proprietary giants in complex coding and research benchmarks while reshaping how developers approach data retrieval.
LLMLG AI Research has released EXAONE 4.5, an open-weight 33B multimodal model featuring a massive 262K context window and a proprietary vision encoder. Discover how its architecture outperforms major proprietary models in STEM and visual reasoning.
Deep LearningLPM 1.0 has taken Hugging Face by storm, fundamentally changing how we generate interactive digital humans. This deep dive explores the architecture that enables infinite-length, real-time video synthesis without identity degradation.
Machine LearningHugging Face's Sentence Transformers v5.4 update introduces native support for multimodal embeddings and rerankers. Discover how to build unified RAG pipelines that process text, images, audio, and video through a single elegant API.
Deep LearningMIT researchers have unlocked a revolutionary way to dynamically prune State-Space Models during training. By applying control theory to discard unobservable states, CompreSSM drastically reduces compute overhead without sacrificing performance.
Deep LearningNVIDIA has open-sourced AITune to automatically evaluate and select the fastest inference backend for PyTorch models. This new toolkit eliminates the optimization gap between research and production environments.
