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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LLMHugging Face's new LoRA-Dash library solves the multi-tenant LLM serving bottleneck by enabling dynamic adapter merging at inference. Developers can now host hundreds of customized AI agents concurrently on a single GPU with near-zero VRAM overhead.
Deep LearningSenseNova-U1 eliminates traditional vision encoders and VAEs to process pixels and text end-to-end. Discover how the NEO-unify architecture sets a new standard for interleaved reasoning and generation.
Deep LearningTencentARC's newly released Pixal3D model solves the fidelity crisis in single-image 3D generation. By utilizing back-projection conditioning to explicitly map 2D pixels directly to 3D coordinates, it achieves unprecedented reconstruction-level quality without losing critical texture details.
Machine LearningPrior Labs just released TabPFN-3, bringing transformer-based in-context learning to enterprise tabular data. This foundation model rivals heavily tuned XGBoost models right out of the box while drastically reducing experimentation time.
Deep LearningDiscover how a joint research team from Google and Meta is using AI agents to dynamically optimize LLM reasoning. AutoTTS cuts inference token usage by 70 percent while matching the accuracy of massive parallel reasoning chains.
Deep LearningExplore how DeepAFM leverages deep learning to denoise High-Speed Atomic Force Microscopy images, bridging the gap between static structural predictions and real-time protein dynamics.
Deep LearningResearchers at Concordia University have introduced Segment-Any-Crack, a revolutionary AI model for infrastructure inspection. By fine-tuning less than 0.05% of its parameters, the system achieves unprecedented accuracy while drastically reducing compute costs.
LLMOpenBMB's new 1.3B parameter multimodal model runs natively on mobile devices while beating larger models in intelligence density. Learn how its novel mixed visual token compression reduces vision computation by over 50 percent.
LLMGoogle's new TurboQuant breakthrough compresses the LLM KV cache to an unprecedented 3 bits without losing model accuracy. This shrinks massive 42GB memory footprints down to just 7GB, fundamentally altering the hardware economics of long-context inference.
