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Image generation has become a fascinating field in AI, offering tools to create astounding visuals with minimal effort. Flux AI image generation model, an open-source model developed by Black Forest
Supervised Learning has been dominant for years, but its reliance on labeled data—a costly and time-consuming resource—creates challenges, especially in areas like medical imaging. On the other hand, Unsupervised Learning,
Stable Diffusion 3.5, released on June 2024 by Stability AI, is the third iteration in the Stable Diffusion family. The Turbo-Large and Large variants of the SD3.5 family are Stability
The NVIDIA AI Summit 2024, held from October 23 to 25 at the Jio World Convention Centre in Mumbai, marked a significant milestone in India’s journey toward becoming a global
This articles discussed Training 3D U-Net for Brain Tumor Segmentation - BraTS2023. Glioma Detection It touches upon the importance of 3D U-Net over 2D U-Net for MRI Brain Scans.
YOLO11 is here! Continuing the legacy of the YOLO series, YOLO11 sets new standards in speed and efficiency. With enhanced architecture and multi-task capabilities, it outperforms previous models, making it
DINO is a self-supervised learning (SSL) framework that uses the Vision Transformer (ViT) as it’s core architecture. While SSL initially gained popularity through its use in natural language processing (NLP)
The article primarily discusses capabilities Sapiens a foundational human vision model by meta, achieves state-of-the-art performance in tasks like 2D pose estimation, body-part segmentation, normal and depth estimation.
In this article, we explore how to build a movie recommendation system using vector search with Qdrant. You'll learn about vector databases, sparse and dense vectors, and how the Retrieval-Augmented
This article gives an overview about the key research papers and dataset from CVPR 2024 along with repository links.
This article discusses how to use any finetuned yolov8 pytorch model on oak-d-lite device with OpenVINO IR Format.
This research article explains a data-centric fine-tuning approach using YOLOv10 models for kidney stone detection.
