JEPA Series Part 4: Semantic Segmentation Using I-JEPA
Semantic segmentation using I-JEPA for brain tumor segmentation on the BRISC-2025 dataset with the PyTorch framework.| DebuggerCafe
In this article, we create a background replacement application using BiRefNet. We cover the code using Jupyter Notebook and create a Gradio application as well. The post Background Replacement Using BiRefNet appeared first on DebuggerCafe.| DebuggerCafe
In this article, we explore the BiRefNet model for high-resolution dichotomous segmentation. Along with discussing the key elements of the paper, we also create a small background removal codebase usign the pretrained model. The post Introduction to BiRefNet appeared first on DebuggerCafe.| DebuggerCafe
Semantic segmentation using I-JEPA for brain tumor segmentation on the BRISC-2025 dataset with the PyTorch framework.| DebuggerCafe
Carrying out DINOv2 segmentation experiments for fine-tuning and transfer learning and comparing the results.| DebuggerCafe
Modifying the DINOv2 model for semantic segmentation and training the model on the Penn-Fudan Pedestrian Segmentation Dataset.| DebuggerCafe
This article explores the history of self-supervised learning, introduces DINO Self-Supervised Learning, and shows how to fine-tune DINO for road segmentation| LearnOpenCV – Learn OpenCV, PyTorch, Keras, Tensorflow with code, & tutorials
