About The Dolphinne Technologies

At dolphinne, we are at the forefront of innovation, specializing in Artificial Intelligence, cutting-edge Web Development, and seamless Mobile Application Development. With a strong focus on utilizing the latest technologies, we provide tailored solutions that drive growth and success for businesses of all sizes. Our expertise extends to Cloud Services, enabling scalable and secure infrastructures, while also excelling in Desktop App Development using Electron.js to create powerful cross-platform solutions. Additionally, we offer comprehensive SEO services, ensuring our clients achieve maximum visibility in the digital landscape. At dolphinne, we are dedicated to transforming ideas into impactful digital experiences, helping businesses thrive in the ever-evolving tech world.

Locations

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Headquarter
  • Location

    A/M/5 Raj Plaza Near Sonal Cross Roads Gurukul Road Ahmedabad Ahmedabad Gujarat India 380052

  • Location

    +919904918167

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Focus Area

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The Dolphinne Technologies's Technical Expertise

iOS - iPhone 20%
Android 20%
Flutter 20%
React Native 40%

Focus Client

  • Small Business (<$100000) 100%

The Dolphinne Technologies's Industry Expertise

  • Advertising & Marketing 10%
  • Business Services 10%
  • Government 10%
  • Healthcare & Medical 10%
  • Information Technology 10%
  • Retail 10%
  • Ecommerce 10%
  • Startups 10%
  • Productivity 10%
  • Food & Beverages 10%

Portfolio

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SAM2 ft. Streamfog

SAM2 ft. Streamfog Description The Segment Anything 2 (SAM2) ft. Streamfog Reimplementation project is designed to offer a simplified and enhanced UI for video segmentation, providing real-time video object tracking. Leveraging React Vite for the frontend and Python for the backend, this project focuses on improving accessibility while maintaining the accuracy of video segmentation tasks. It was developed in collaboration with Streamfog, emphasizing innovative solutions in video technology. Machine Learning Object Tracking Web App Technologies React.js Vite.js Python AWS EC2 TensorFlow Challenges Reimplementing Meta's SAM2 architecture presented significant challenges, especially when adapting it to create a user-friendly version with a more intuitive interface. One of the main hurdles was simplifying the underlying complexity of SAM2's memory mechanism, mask propagation, and occlusion handling, ensuring users could interact with it seamlessly without sacrificing performance. The need to maintain the robustness of SAM2’s real-time video segmentation while making the UI accessible for users who may not be familiar with complex AI tools added another layer of difficulty. Additionally, hosting a high-performance backend capable of handling heavy segmentation operations in real time was critical, especially when scaling across longer videos and more diverse object classes. Another challenge was ensuring the system could accurately handle ambiguity and occlusion during video segmentation, just as SAM2 does in more sophisticated environments. Balancing computational efficiency with accuracy was key, particularly given the system's need to operate in real-time, with a smooth experience across different devices and browsers. The requirement for real-time response while integrating high-performance AI models in the backend was especially difficult, given the use of AWS EC2 to handle large-scale operations. Solutions To address these challenges, the project retained much of SAM2’s underlying architecture, while focusing on enhancing usability by refining the interface. The React Vite frontend was designed to prioritize a smooth user experience, allowing users to easily input prompts, interact with video frames, and adjust segmentation masks. The lightweight nature of Vite allowed for efficient handling of frontend interactions, minimizing lag during operations. On the backend, Python was employed for managing segmentation logic, alongside AWS EC2 for robust, scalable performance. The backend system was fine-tuned to optimize response time without compromising accuracy, particularly when managing SAM2’s memory and mask propagation capabilities across video frames. Integrating AWS services ensured that the system could handle a large volume of segmentation tasks while maintaining seamless synchronization between frontend actions and backend computations. Overall, these strategies enhanced the performance and accessibility of the project while maintaining the core functionalities of SAM2. Results The reimplementation successfully resulted in a more accessible version of Meta's SAM2, with a user-friendly interface that simplified complex segmentation tasks. Users could interact with the system in real-time, generating accurate segmentation masks across video frames without needing to understand the intricacies of SAM2's underlying architecture. This led to faster, more efficient video annotations and improved ease of use for individuals working with video segmentation tasks. The project also improved scalability, handling large video datasets and producing accurate segmentation even under difficult conditions, such as object occlusion and ambiguity. The use of AWS EC2 for hosting provided reliable, high-performance backend operations, ensuring the system could handle multiple concurrent segmentation requests with minimal delay. As a result, the reimplementation not only maintained the performance and accuracy of Meta’s SAM2 but also significantly improved the overall user experience. Duration: 2 months GO TO SITE LINK Read More

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Industry

Advertising & Marketing

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Budget

Not Disclosed

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Timeline

8 weeks

SAM2 ft. Streamfog

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