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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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.
LLMDiscover how combining Nvidia's compact Nemotron-3-Nano model with WebGPU acceleration enables powerful, private, and real-time AI inference directly inside your web browser.
Deep LearningExplore the internal mechanics of MegaTrain, the trending deep learning framework that leverages extreme host memory offloading and asynchronous streaming to train massive foundation models on a single GPU.
LLMZhipu AI has open-sourced GLM-5.1, a massive 744-billion-parameter MoE model under the MIT license. Explore how it outperforms proprietary giants on software engineering benchmarks and what it means for the future of open-weight AI.
