#Image Segmentation

This repository provides a detailed evaluation of 28 superpixel algorithms utilizing 5 datasets to assess visual quality, performance, and robustness. It acts as a supplemental resource for a comparison published in Computer Vision and Image Understanding, 2018. Key updates include Docker implementations and evaluations of average metrics. The repository allows for fair benchmarking by optimizing parameters on separate training sets, focusing on metrics such as Boundary Recall and Undersegmentation Error.

Flash provides a comprehensive toolkit for implementing AI with user-friendly mechanisms to address over 15 tasks across 7 data domains. It offers streamlined data processing, model setup, and fine-tuning, ideal for researchers and developers aiming for production-grade AI solutions. With options for pretrained models, tailored training strategies, and optimizers, users can build and deploy AI models effectively. Flash supports various customization options like transformation hooks and a range of optimizers and schedulers, providing a versatile and adaptable AI framework perfect for real-world applications. Simplified AI recipes support contribution and collaboration within the community.

The SAM-Adapter-PyTorch project improves segmentation in difficult scenarios, including camouflage, shadows, and medical images, using the SAM2-Adapter. It supports diverse datasets and GPU configurations, emphasizing adaptability. The integration of SAM2 as a backbone boosts performance and application scope. Access to pre-trained models and thorough dataset links provides a solid research foundation, advancing versatile segmentation solutions.

The DIS project highlights significant innovations in dichotomous image segmentation, including the introduction of the DIS5K V1.0 dataset and upcoming DIS V2.0 dataset with broader category inclusion. Recognized at ECCV 2022, it features an optimized IS-Net model suited for general use, available through platforms like Huggingface and Bohrium for live demonstrations. The project supports diverse applications from 3D modeling to AR, driven by advanced IS-Net architecture and human correction efforts, demonstrating its role in advancing image processing capabilities.

Discover the advancements in Mamba models highlighting the application of state space models across medical imaging and AI. Topics extend to image enhancement, video and natural language processing, multi-modal comprehension, and 3D recognition. Explore detailed surveys and architecture updates focusing on effective data analysis solutions. Understand the tools driving innovation in computational imaging and AI healthcare as featured in our survey of efficient models for medical imaging.

Medical SAM 2 utilizes the SAM 2 framework to perform both 2D and 3D medical image segmentation with improved accuracy and efficiency, mimicking video processing. It is applicable to a wide range of cases, including optic-cup and multi-organ abdominal segmentation. The model offers downloadable pre-trained weights and datasets for practical use, accompanied by clear installation and usage instructions. Its prompt-based approach enhances adaptability and practicality across diverse medical imaging scenarios.

Explore the influence of the 'Segment Anything' open-source project on advancing computer vision. This informative resource compiles cutting-edge research, emphasizing its broad application in image segmentation, video interpolation, and medical imaging. The repository collects foundational insights and recent developments, seamlessly integrating SAM with other innovations. It effectively meets challenges in visual segmentation and camouflaged object detection, ensuring comprehensive knowledge and updates across various fields.

Explore a Python library designed for efficient image segmentation with PyTorch. Create models quickly using its high-level API, choose from 10 architectures like Unet, and leverage 124 pre-trained encoders with access to over 500 more via the timm library. Supports both binary and multi-class segmentation with optimized preprocessing and popular metrics.

The BiRefNet project enhances image segmentation using bilateral reference techniques, delivering cutting-edge performance in areas such as dichotomous segmentation, camouflaged detection, and salient object identification. It features models for high-resolution input and custom data fine-tuning. Explore demos on Hugging Face and Google Colab. Collaborative efforts are sought for GPU resources to further project advancements, especially for matting tasks.