Deep Learning
SANA-Sprint: Get high-quality (1024, 1024) AI images in a single step! Learn about this ultra-fast diffusion model transforming image generation & real-time AI.
Ever watched an AI-generated video and wondered how it was made? Or perhaps dreamed of creating your own dynamic scenes, only to be overwhelmed by the complexity or the need
In Deep Learning, Batch Normalization (BatchNorm) and Dropout, as Regularizers, are two powerful techniques used to optimize model performance, prevent overfitting, and speed up convergence. While both have their individual
As AI systems become more specialized, getting them to work together without endless glue code is the next big challenge. That’s where Google’s A2A Protocol (Agent-to-Agent) steps in—a standardized messaging
GPT-4o image generation is a game-changer! With native support in ChatGPT, you can now create stunning visuals from text prompts, refine them, and explore styles like Studio Ghibli or photorealism.
A comprehensive step-by-step guide on fine-tuning RetinaNet using PyTorch to achieve 79% accuracy on wildlife detection tasks. In this tutorial, we dive deep into RetinaNet’s architecture, explain the benefits of
AI, being no longer confined to passive algorithms, is transforming itself into autonomous agents that can perceive, reason, and act with increasing intelligence. These agents are designed to navigate uncertainty,
Leaf diseases reduce crop yields and impact food security. Finetuning SAM2 helps detect and segment diseased areas using deep learning. With a small dataset, we achieved 74% IoU, making early
Apple's DepthPro is quite impressive, producing pixel-perfect, high-resolution metric depth maps with sharp boundaries through monocular depth estimation. It outperforms all of its contenders like Metric3D v2 and DepthAnything in
Recently, the interest in fine-tuning Stable Diffusion models has surged among AI enthusiasts and professionals, driven by the need to incorporate these models into specific requirements. This article walks you
Image Captioning using ResNet and LSTM bridges vision and language, enabling machines to "see" images and "describe" them in text. This model powers applications like accessibility for visually impaired users,
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,
