Image Segmentation
MedSAM2 brings “segment anything” power to healthcare, carving organs, tumours, and even moving heart chambers from CT, MRI, PET, and live ultrasound with a single prompt. Running in < 1
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
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)
In this article, we explore SAM 2 (Segment Anything Model 2), for Promptable Visual Segmentation of objects in images and videos.
U2-Net (popularly known as U2-Net) is a simple yet powerful deep-learning-based semantic segmentation model that revolutionizes background removal in image segmentation. Its effective and straightforward approach is crucial for applications
