In modern machine learning workflows, teams often face a tough trade-off: spend days or weeks manually tuning architectures and hyperparameters,…
GhostNet: High-Accuracy Vision Models with Minimal Compute for Edge Deployment 4355
Overview Deploying powerful computer vision models on resource-constrained devices—such as smartphones, IoT sensors, or drones—has long been a major engineering…
EfficientViT-SAM: Real-Time, High-Accuracy Image Segmentation Without Compromise 3102
If you’ve worked with Meta’s Segment Anything Model (SAM), you know its power—and its pain points. While SAM delivers state-of-the-art…
Bitnet.cpp: Run 1.58-Bit LLMs at the Edge with Lossless Speed and Efficiency 24456
Large language models (LLMs) are becoming increasingly central to real-world applications—but their computational demands remain a major barrier for edge…
SWE-Lancer: Benchmark Real-World Freelance Coding Tasks to Measure LLMs’ True Engineering Value 1438
Evaluating large language models (LLMs) on synthetic coding benchmarks often fails to reflect their real-world utility. Enter SWE-Lancer—a rigorously constructed…
Open-Sora Plan: Open-Source High-Quality Long Video Generation for Real-World Applications 12044
Open-Sora Plan is an open-source initiative designed to democratize access to state-of-the-art video generation capabilities. Inspired by the promise of…
MoBA: Efficient Long-Context Attention for LLMs Without Compromising Reasoning Quality 2014
Handling long input sequences—ranging from tens of thousands to over a million tokens—is no longer a theoretical benchmark but a…
TextBox 2.0: A Unified Library for Rapid Text Generation with Pre-Trained Language Models 1096
If you’ve ever struggled to compare BART, T5, and a custom Chinese language model on summarization, translation, or dialogue generation—only…
ZoeDepth: Metric-Accurate, Zero-Shot Monocular Depth Estimation for Real-World Applications 2755
Depth estimation from a single RGB image—monocular depth estimation—is a foundational task in computer vision with far-reaching implications in robotics,…
VAD: Vectorized End-to-End Autonomous Driving for Faster, Safer Planning 1159
Autonomous driving systems must balance accuracy, safety, and real-time performance. Traditional approaches often rely on dense rasterized representations of the…