Object Recognition via Google Lens

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@amirrmusavifr

Hi, I’m an AI expert with professional experience in computer vision, with a proven track record of working on complex image processing and AI/ML model development. With skill sets: • Algorithm Development: Strong understanding of computer vision algorithms and techniques, including convolutional neural networks (CNNs), object detection, image segmentation and feature extraction. • Model Training & fine-tuning: Develop and train machine learning models tailored for image analysis and visual data interpretation. I have worked on some well-known models like YOLO, RCNN, U-Net, Deeplab, ViT etc. • AI Integration: Implement and integrate AI models into existing software and hardware systems, ensuring high performance and scalability. • Data Analysis: Analyze and process large datasets of images and video feeds to identify patterns, trends, and insights. • Data Handling: Experience in handling and processing large datasets, including image and video data. Familiarity with data augmentation techniques and synthetic data generation. • Performance Optimization: Optimize algorithms and models for real-time processing and ensure they can handle large-scale data efficiently. • Programming Skills: Proficient in programming languages such as Python. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. • Tools & Libraries: Proficiency with OpenCV, scikit-image, and other relevant libraries. Experience with version control systems like Git.

@NEHABHAT92

As an accomplished developer with nine plus years of experience, I have gained remarkable expertise in the areas of Android and PHP that serve as the backbone of your project. My team and I can build a high-caliber prototype for you, which draws bounding boxes around the detected objects/products and delivers corresponding information panels- all key attributes you've outlined. Not only will we provide the working model, but also contribute model files, inference code, clear documentation, and an extensive evaluator's guide to retrain categories at any time in the future. Being proficient in E-commerce and CMS based website development, I understand that recognition speed and precision are pivotal factors in this project. With our collaborative efforts, you can expect 90%+ top-1 accuracy from our Object Recall model. Additionally, my familiarity with useful tools like Google Cloud Vision, TensorFlow, PyTorch, OpenCV adds on to our adeptness in delivering accurate outcomes through modern frameworks for mobility or web backend. As an added bonus to choosing me for your project is long-term support plus affordable hosting services. We emphasize not just delivering a product but a reliable IT service ensuring that projects we undertake turn our client's vision into reality. So let's step ahead on this adventure together where my skills meet your goals!

@Web1devloper

Leveraging my eight years of experience as a data analyst and scientist with a specialization in data storytelling, predictive analytics, and machine learning, I am confident that my skill set is uniquely poised to tackle your "Google-Lens style" object recognition project. Throughout my career, I've successfully optimized operations, enhanced customer understanding, and forecasted outcomes for businesses across various industries that mirror your objectives. My familiarity with Google Cloud Vision, TensorFlow, PyTorch, OpenCV will facilitate the design, training, and integration of the object-detection model you need. Additionally, my proficiency with and clear documentation of the technologies I employ during development - Python and associated tools (Pandas, NumPy, Scikit-learn) – will provide you a user-friendly end-result that runs both quickly and accurately on your preferred platform. Not only can you expect a working prototype but also all the model files and inference code you'll require to retrain with additional categories later. Rest assured that I prioritize code clarity; my work emphasizes ease-of-use and accessibility. Lastly, in line with your desire for transparency and efficient communication, I possess excellent problem-solving abilities as well as effective stakeholder communication.

@mayankinnovative

Hello I have 15+ years of experience in software development, including mobile apps and AI/ML solutions. I can develop your Google Lens-style feature to recognize everyday objects and consumer products with high accuracy and speed. My approach: - Build and train an object-detection model using TensorFlow/PyTorch/OpenCV for fast, lightweight inference on mobile or web. - Integrate the model with a responsive UI that draws bounding boxes around detected objects and displays concise information, specs, and metadata. - Deliver fully documented model files, inference code, and a setup guide so you can retrain with additional categories. - Optimize for recognition speed and precision (90%+ top-1 accuracy) with clear, maintainable code. I’ll provide a working prototype for Android/iOS/web, a short accuracy report, and full documentation. I’ve delivered similar ML-powered recognition projects before and can ensure smooth deployment and extensibility for future features.

@ASSAMWEB

Greetings, I have read the project description I have been working on a similar project in recent time. I am interested in the work open a chat to discuss requirements in details.

@gsinfotechopcp4

As a seasoned freelancer with over 8 years of experience in application, games, and software development, I can confidently say I am well-suited for the task at hand. My proficiency in using Google Cloud Vision, Tensorflow, and OpenCV for object recognition aligns perfectly with your project's demands. I have a proven track record of developing efficient and accurate image analysis models that would amp up your project's precision. What sets me apart from other candidates is my appreciation for clean code. Not only will I deliver a working prototype for Android, iOS, or web that executes tasks quickly and accurately on any device or backend, but I'll also ensure to document every step of the process comprehensively for future retraining purposes. In terms of similar projects shipped, my portfolio boasts an AI-driven inventory management system that uses object recognition to identify products in real-time. It's quite similar to what you're aiming for with this project. My dedication to designing intuitive applications coupled with my overall commitment to quality make me the obvious choice for this important task. Let's bring your vision to life together!

@angelr218

I'm developing a feature similar to Google Lens that can recognize everyday objects and consumer products from photos or live camera feeds. The goal is to quickly return detailed information about each recognized item, without text extraction, translation, or AR overlays for now. You'll design and train the object-detection model, then integrate it with an info layer that shows descriptions, specs, and metadata. The final solution needs to be fast and accurate, working on either mobile (Android/iOS) or a web backend. What I Need: A working prototype (Android, iOS, or web) that detects objects/products and shows info panels Model files, inference code, and documentation for retraining with new categories A setup guide and a short report on model accuracy and next steps Success Criteria: High speed and accuracy (aiming for 90%+ top-1 accuracy) Clear, maintainable code Let me know which stack you would use and any similar projects you've worked on This version keeps the main points, but in a simpler, more straightforward tone. Let me know if you'd like any more tweaks!

@HskHarmanSingh

Hello, Your Google Lens–style object recognition feature fits very well with my experience in computer vision, object detection, and ML integration for mobile and web systems. I can design and deliver a fast and accurate object-detection pipeline that processes images or live camera feeds, draws bounding boxes around detected objects/products, and displays a clean information panel using your provided metadata. The scope will remain strictly limited to image recognition and analysis, with no OCR, translation, or AR overlays. Proposed stack (flexible): • YOLOv8 / EfficientDet (TensorFlow or PyTorch) • OpenCV for vision processing • Python backend (FastAPI) or Google App Engine • Android integration using CameraX (or web frontend) Deliverables: • Working prototype (Android or web) with real-time detection • Trained model files and inference code • Clear documentation for retraining with new categories • Setup guide and a short report covering accuracy, performance, and next steps I’ll optimize for low-latency inference and high precision, targeting 90%+ top-1 accuracy on your test dataset, while keeping the codebase clean and extensible. Happy to discuss dataset details, deployment preferences (on-device vs backend), and performance trade-offs. Best regards, Harman

@ThynkLoop

We are Thynk Loop, and I can build this as a clean, focused object-recognition feature that feels fast and reliable, without drifting into extra AR or OCR scope. The emphasis will be on accurate detection, clear results, and a setup you can extend later with new product categories. I’ll design and train an object-detection model suited to your target platform, using a proven stack such as PyTorch or TensorFlow with an efficient architecture that runs well on mobile or a lightweight backend. The model will detect everyday objects and consumer products, draw bounding boxes, and pass the results into a simple information layer that displays descriptions and metadata you define. The pipeline will be structured so retraining or adding classes is straightforward. You’ll receive a working prototype, either as a mobile app or a web-based demo, that takes photos or camera input and shows detections with info panels in real time. I’ll deliver the trained model files, inference code, and clear documentation explaining how to retrain and tune accuracy. A short report will summarise performance on your test set, current precision, and practical next steps to push accuracy further. If this aligns with what you’re building, I can outline the exact stack I recommend and share examples of similar vision systems I’ve worked on, along with a realistic timeline to reach a first demo.

@sisodiya01

Hello, I’ve carefully reviewed your requirement for a Google Lens–style object recognition system, and I find the project very interesting and well-defined. I can help you build a clean and efficient object detection solution that recognizes everyday objects/products from images or live camera feeds and returns structured metadata in real time. My focus will be on accuracy, speed, and clean integration. Here’s how I would approach the project: • Use a proven object detection model (YOLO / TensorFlow / OpenCV based, depending on your preference) • Train and fine-tune the model on your provided categories • Output bounding boxes with detected object labels • Build a lightweight backend or mobile-compatible pipeline • Provide clear documentation so the model can be retrained or extended later Deliverables you’ll receive: • A working prototype (mobile/web/backend as required) • Trained model + inference code • Setup & retraining documentation • A short report with accuracy metrics and next-step suggestions I value clear communication and will keep you updated at every stage. Before starting, I’d like to clarify: 1) Target platform (Android / iOS / Web / Backend)? 2) Number of object categories initially? 3) Do you have a dataset