In this article, we will delve into the mechanics of monocular depth estimation, exploring the neural network architectures used, the techniques for training and improving these models, and the practical applications of this exciting field ..read more

In this article learn about Panoptic segmentation, an advanced technique offers detailed image analysis, making it crucial for applications in autonomous driving, medical imaging, and more ..read more

Retrieval-augmented generation (RAG) allows large language models (LLMs) to answer questions using external knowledge sources. Still, it needs help with global questions about entire text corpora, such as identifying main themes. Microsoft's recent research proposed Graph RAG in April, which combines RAG and Query Focussed Summarization or QFS methods for scalable question answering over private text corpora. Graph RAG uses an LLM to create a graph-based text index, deriving an entity knowledge graph and generating community summaries of related entities. When a question is asked, these summa ..read more

Modern large language models (LLMs) are very powerful and becoming more so by the day. However, their power is generic: they are good at answering questions, following instructions, etc., on public content on the internet, but can rapidly fall away when dealing with specialized subjects outside of what they were trained on. They either do not know what to output, or worse, fabricate something incorrect that can appear legitimate. For many businesses and applications, it is in precisely this realm of specialized information that an LLM can provide its greatest value-add, because it allows thei ..read more

This article will discuss Depth Anything V2, a practical solution for robust monocular depth estimation. Depth Anything model aims to create a simple yet powerful foundation model that works well with any image under any conditions. The dataset was significantly expanded using a data engine to collect and automatically annotate around 62 million unlabeled images to achieve this. This large-scale data helps reduce generalization errors. This powerful model uses two key strategies to make the data scaling effective. First, a more challenging optimization target is set using data augmentation to ..read more

DETR (Detection Transformer) is a deep learning architecture first proposed as a new approach to object detection. It's the first object detection framework to successfully integrate transformers as a central building block in the detection pipeline. DETR completely changes the architecture compared with previous object detection systems. In this article, we delve into the concept of Detection Transformer (DETR), a groundbreaking approach to object detection. Join our Discord Community Get started Join the community What is Object Detection? According to Wikipedia, object detection is a co ..read more

Introduction YOLO (You Only Look Once) is a popular object detection algorithm that has revolutionized the field of computer vision. It's fast and efficient, making it an excellent choice for real-time object detection tasks. YOLO NAS (Neural Architecture Search) is a recent implementation of the YOLO algorithm that uses NAS to search for the optimal architecture for object detection. In this article, we will provide a comprehensive overview of the architecture of YOLO NAS, highlighting its unique features, advantages, and potential use cases. We will cover details on its neural network desig ..read more

In today's world, the use of artificial intelligence and machine learning has become essential in solving real-world problems. Models like large language models or vision models have captured attention due to their remarkable performance and usefulness. If these models are running on a cloud or a big device, this does not create a problem. However, their size and computational demands pose a major challenge when deploying these models on edge devices or for real-time applications. Devices like edge devices, what we call smartwatches or Fitbits, have limited resources, and quantization is a pr ..read more

In the current era of AI and machine learning, the NVIDIA Tensor Core GPU stands out as a powerhouse, offering exceptional performance, intuitive operation, and top-quality security features to tackle AI and ML workloads. With its dedicated Transformer Engine specially designed to handle large language models and leveraging the cutting-edge NVIDIA Hopper architecture, it significantly boosts AI performance, delivering a remarkable 30x speed enhancement for large language models compared to previous versions. This advancement is truly exciting and promising for the future of AI technology! Is ..read more

Artificial intelligence (AI) can solve some of the world's biggest problems, but only if everyone has the tools to use it. On Jun 27, 2024, Google, a leading player in AI technology, launched Gemma 2 9B and 27B—a set of lightweight, advanced AI models. These models, built with the same technology as the famous Gemini models, make AI accessible to more people, marking a significant milestone in democratizing AI. Gemma 2 comes in two sizes: 9 billion (9B) and 27 billion (27B) parameters, and comes with a context length of 8K tokens. Google claims the model performs better and is more efficient ..read more