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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Deep LearningResearchers have cracked one of the most notoriously unstable problems in mathematics by introducing Mollifier Layers to neural networks. This mathematically refined architecture smooths noisy data to reverse-engineer hidden causes in fields ranging from weather prediction to genetics.
LLMVector embeddings struggle with complex, structured documents by destroying their natural hierarchy. Learn how PageIndex uses an active LLM agent to navigate document trees, recently achieving an unprecedented 98.7% accuracy on FinanceBench.
Machine LearningMIT CSAIL's new Federated Tiny Training Engine slashes memory and bandwidth overhead, enabling billions of highly constrained IoT devices to participate in privacy-preserving distributed AI training.
Deep LearningNVIDIA has released GR00T N1.5 to democratize embodied AI with an open vision-language-action model. By combining an Eagle vision backbone with a diffusion transformer action head and native LeRobot integration, this release rewrites the playbook for humanoid control.
Deep LearningUI-DETR-1 establishes a new state-of-the-art for visual computer interaction agents by achieving 70.8 percent accuracy on the WebClick benchmark. This real-time perception model bypasses traditional DOM parsing to enable robust multi-action task automation across varied interfaces.
Machine LearningML-Master 2.0 solves the long-horizon memory bottleneck in AI agents by introducing Hierarchical Cognitive Caching. By achieving a 56.44 percent medal rate on OpenAI's MLE-Bench, it signals a massive paradigm shift in end-to-end autonomous research.
LLMNeutrino-Instruct 7B is a brand new open-source language model trained entirely from scratch by an independent 23-year-old researcher. Available under the Apache 2.0 license, this milestone challenges the assumption that only massive tech conglomerates can build state-of-the-art foundation models.
Deep LearningHarvard researchers have cracked a fundamental mystery of deep learning using statistical physics and renormalization theory. Discover how this mathematical framework explains why massive neural networks generalize perfectly instead of memorizing their training data.
Deep LearningMicrosoft Research's World-R1 framework drastically improves physical realism and scene permanence in AI video generation. By leveraging reinforcement learning and specialized datasets, it forces models to respect 3D constraints without massive compute overhead.
