Restaurant Table Management with AI

@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 Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .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

@jaroslavcaprata

Hi, I can build this as an edge-based computer vision pipeline designed specifically for restaurant table monitoring rather than as a generic object-detection demo. I’d use Roboflow for dataset management, annotation, augmentation, and model iteration, then deploy the YOLOv12 model on the NVIDIA Jetson with inference optimized for real-time performance. The detection layer can identify the required food/table objects and expose structured events that your seating-management logic can use—for example, occupied tables, active meals, cleared tables, or configurable food-item states. I’ll also handle confidence thresholds, duplicate detections, camera positioning considerations, logging, and model performance validation so the system behaves reliably in real restaurant conditions. The implementation can be structured so new food classes or additional cameras can be added later without redesigning the entire pipeline. I’m comfortable with Python, computer vision workflows, YOLO-based detection, model integration, and production AI deployment, and I can work directly with your existing Jetson/camera setup.

@AlonzoLeeeooo

Will your cameras watch each table continuously, and how should a detected food item be linked to a specific table — per-frame detections, or tracking dishes over time so the system knows when a table actually frees up? My research areas include deep-learning deployment, and since 2024 I have assisted several startups with production AI deployments — including the accuracy–latency–compute tradeoff your post implies when it asks for real-time seating management on NVIDIA Jetson. That background is directly relevant to wiring YOLOv12 detections into table or seating management logic. One gap in your requirements: you specify Roboflow and YOLOv12 on Jetson, but Jetson hardware needs TensorRT export and FP16/INT8 quantization for the model to hold real-time frame rates. That conversion and on-device benchmarking step is missing from your spec, and I would include it so the Roboflow-trained weights actually perform on the device instead of only in the notebook. Milestone 1 (days 1–7): Roboflow dataset pipeline, YOLOv12 trained on your food-item classes, detection validated on sample footage. Milestone 2 (days 8–12): Jetson deployment — TensorRT-optimized inference plus the table-status logic linking food items to tables. What does your camera setup look like — fixed overhead per table, or a shared view? Chang

@adriank06

YOLOv12 + Roboflow + Jetson for restaurant table management — this is my stack. Hi, I've built computer vision pipelines with YOLO models deployed on edge hardware. For this system I'd train a food item detection model via Roboflow, optimize it for Jetson with TensorRT, and build the table management logic on top — tracking which tables have active food items, flagging cleared tables, and surfacing that status in real time. Approach: Train/fine-tune YOLOv12 on food item dataset via Roboflow Optimize inference for Jetson using TensorRT + DeepStream Build table occupancy logic based on detection zones per table Timeline: 10–14 days One question: do you have an existing food item dataset, or do you need data collection and labeling as part of the scope?

@gi2br3n1906

Automated restaurant table turnover requires low-latency edge vision. I deploy custom YOLOv12 models optimized via TensorRT for NVIDIA Jetson. Here is how I will engineer your seating and dining analytics pipeline: Dataset Pipeline (Roboflow): Structure and augment classes for food items, serveware, and cleared tables across varying dining angles and lighting conditions. YOLOv12 Training: Fine-tune YOLOv12 to detect food presence and classify active table phases (e.g., food served, dining in progress, finished/empty dishes, vacant). Jetson Edge Optimization: Export trained weights to ONNX and build an FP16/INT8 TensorRT engine on the Jetson to maintain steady real-time FPS on live RTSP feeds without thermal throttling. Table State Logic: Connect inference outputs to table state event triggers (e.g., auto-flagging tables needing busser service to accelerate seating turnover). Execution Milestones: Roboflow Dataset Schema & Preprocessing Setup YOLOv12 Fine-Tuning & Detection Validation TensorRT Export & Jetson Hardware Benchmarking Table Management State Logic & Stream Testing My core focus is on YOLO vision architectures, model optimization, and edge inference. Please review my profile to inspect my background in vision systems and deployment pipelines. Which specific NVIDIA Jetson board (Nano, Xavier, or Orin) are you using, and what is your camera feed resolution?

@nexvative

I can build this restaurant seating/table-management computer vision system using Roboflow, YOLOv12, and NVIDIA Jetson. I have hands-on experience building real-time computer vision systems for edge devices, including a crowd detection and geo-tracking system deployed on Raspberry Pi 5 + Hailo-8, using RTSP video, person detection/tracking, and real-time telemetry. I’ve also worked with OpenCV, TensorFlow/PyTorch, GPU-accelerated inference, and camera-based monitoring systems. For your project, I can set up the Roboflow dataset/annotation pipeline, train and optimize the YOLOv12 model for your required food-item classes, and deploy an optimized inference pipeline on the NVIDIA Jetson. The system can detect food items from live camera feeds and use those detections as signals for table/seating management, with appropriate tracking and event logic. I’ll focus on reliable real-time inference, Jetson performance optimization, camera integration, and a clean architecture that can be extended later for occupancy, table status, or additional restaurant objects. I can start by reviewing your camera setup, target food classes, and Jetson model, then define the detection pipeline and milestones.

@jagratip3

Hi, I have experience with computer vision, YOLO-based object detection, Roboflow workflows, and NVIDIA Jetson deployment. I can develop a real-time system that detects and identifies food items using YOLOv12, optimized for Jetson hardware, to support restaurant table and seating management workflows. ✔ Roboflow dataset preparation & training ✔ YOLOv12 model development and optimization ✔ NVIDIA Jetson deployment & inference tuning ✔ Real-time food item detection ✔ Performance testing and documentation ✔ Scalable architecture for future enhancements Please share your camera setup, expected food categories, and operational workflow so I can recommend the best approach and timeline.

@ronakkataria1

One thing about the goal before the model. Food on a table tells you it's in use. It can't tell you when it's free. Guests who've left still leave plates behind, and that's exactly when you'd want to seat the next party. So I'd pair food and dish detection with person detection per table zone, and turn that into states like free, seated, eating and needs clearing, with a short time window so one missed frame doesn't flip a table. On YOLOv12 and the Jetson.. table states change over minutes, so a few frames a second per camera is plenty. That leaves room to run v12 at a decent input size and cover several tables from one camera. v12 leans on attention layers, so I'd export it to TensorRT at FP16 and only try INT8 if accuracy holds on your footage. The data is the real work. Public food datasets are close-up dish photos. A ceiling camera sees small plates at an angle, half hidden by people and glasses. So I'd pull frames from your cameras, label them in Roboflow, train there, and export for the device. You'd get the trained model, the Jetson inference service with per-table states, and an accuracy report per class on frames the model has never seen. Which Jetson model is it, and how many tables does one camera cover? Ronak

@TechizeBuilder

Dear Client, I read "Restaurant Table Management with AI" carefully and understand you need a modern, attractive website for your restaurant/cafe that looks great and converts visitors into customers. My experience with AI Model Development, AI Automation aligns well with your requirements. I can build a responsive restaurant website with a modern UI, digital menu, food/category sections, gallery, location & Google Maps, table/contact options, WhatsApp integration, and an easy-to-manage admin panel. A few quick questions to get started: Do you already have the design/branding, or should I create the UI from scratch? Do you need online ordering/reservations along with the website? Thanks & Regards, Deepak

@TeraByte26

Hi Kamal, I will deliver a YOLOv12 computer vision system built on Roboflow, deployed on your NVIDIA Jetson, that detects food items for table‑seating management. I can have a prototype running within 10 days, within your INR 12,500‑37,500 budget. I can share a demo video now, start right away. Any preferred dataset format? Waiting for your response in chat! Best Regards.

@alfatrade2

Food detection alone won't say which seat is free; you'd also need chairs and people in the frame, or a table-state rule such as plate served, plate empty. Which of those is the goal? I haven't deployed YOLOv12 on a Jetson, but I built an outbound calling voice agent with offline Whisper speech-to-text, so running models locally on-device and structuring the output is familiar. Labeled photos from your restaurant will decide accuracy more than the model version.

@pandaymano8

As a Full-Stack Developer and AI Specialist, I believe I have the diverse set of skills needed to tackle your Restaurant Table Management project. My proficiency in technologies like Roboflow, YOLOv12, as well as my experience with the Nvidia Jetson platform make me especially well-suited for this task. My past AI projects have focused on analyzing and identifying complex patterns within large datasets - skills that will equally be applicable in your table management system. Moreover, I understand the intricacies of image recognition and the importance of creating reliable, accurate model. With my expertise in Computer Vision and Deep Learning, I can help build an advanced system capable of not just seating management but also identifying food items - ultimately improving the overall efficiency of your restaurant. Lastly, my enthusiasm lies in building technology that solves real-world problems while ensuring reliability and long-term value. My knack for understanding client requirements and delivering practical solutions is something you can count on. Let's work together to create a robust, scalable, and future-proof table management solution with AI at its core.

@indreshpnk

Hello, I understand you need a computer vision system using Roboflow and YOLOv12 on NVIDIA Jetson to identify food items for restaurant table and seating management. The goal is to deliver an accurate, real-time, edge-based AI solution. Here’s what I can provide: YOLOv12-based food detection and classification trained with Roboflow datasets. Optimised inference and deployment on NVIDIA Jetson for real-time performance. Integration of detection results into a table/seating management workflow with reliable AI automation. I bring over 4+ years of experience in Python, AI/ML, image processing, and full-stack development, with a strong focus on building reliable and scalable applications. I’ve worked on API-driven and AI-integrated solutions, keeping performance, accuracy, and practical deployment in mind. Just to clarify a few things: Which NVIDIA Jetson model are you using, such as Orin Nano, Xavier, or another variant? Do you already have a labelled food-item dataset in Roboflow, or should the dataset preparation and trainin

@Chirag3147

Having worked extensively over the past 4+ years in AI Automation, I am confident that my skills match the needs of your project. I have hands-on experience designing and building computer vision-based systems using Roboflow and YOLO architecture, and have implemented them on platforms like NVIDIA Jetson successfully. In addition to my ML skills, I also bring a solid background in full-stack development, which is crucial for creating scalable systems. Combined with my knowledge of Python to handle back-end operations and JavaScript for smooth web and mobile app interfaces, I can ensure an end-to-end functioning solution for your restaurant table management needs. Last but not least, my previous work underscores my ability to deliver efficient and reliable products - attributes that are essential for this project. Let's combine our efforts to create an AI-powered restaurant table management system that enhances productivity, streamlines processes, and makes your business ready for the future. With me, you'll get someone who not only creates features but develops clean, automated, and scalable solutions to fix operational challenges.

@aimenh9

Here is a shortened version that fits comfortably under the 1,500-character limit (approx. 1,150 characters) while keeping all key details intact: Hello! I can help you build this AI-powered restaurant table management system using YOLOv12, Roboflow, and NVIDIA Jetson. With strong experience in edge-AI and Computer Vision, I am confident in delivering an accurate, real-time solution. Approach & Milestones Data & Training (Days 1–2): Set up Roboflow datasets, annotate food items/tables, apply augmentations, and train the YOLOv12 model. Jetson Optimization (Days 3–4): Export to TensorRT for low-latency inference and maximum FPS on your NVIDIA Jetson board. Logic & Integration (Days 5–6): Implement table/seating zone mapping logic and food detection rules to track occupancy. Testing & Handover (Day 7): Final bench testing, deployment, and documentation. Why Choose Me? Edge AI & Hardware: Direct experience optimizing state-of-the-art YOLO models (v8 through v12) on NVIDIA Jetson platforms (Nano, Xavier, Orin). Roboflow & CV Pipelines: Expert in end-to-end dataset management, custom annotation, and high-speed inference. Smart Automation: Proven track record bridging real-time video feeds with automated tracking metrics. Could you share details on your camera placement (overhead vs. angled) and if you already have labeled data in Roboflow? Best regards, Aimen Hafeez

@rakhshanz3

With over two decades of experience in the tech industry, including a Doctorate in Artificial Intelligence and Computer Science, I am well-versed in pioneering cutting-edge solutions like the one you require. As the previous CTO of an AI startup, my strong background in building advanced digital products and AI tools aligns perfectly with this project. My expertise in image processing and computer vision, combined with extensive proficiency in Roboflow and YOLOv12, will enable me to create a sophisticated system for restaurant table management using the NVIDIA Jetson platform. I have a proven track record of implementing complex AI concepts into functional systems, such as developing voice and chat agents powered by deep learning models. Moreover, my experience in the field also extends to hospitality applications and large-scale software engineering for multinational companies. Therefore, choosing me means getting someone who not only has the technical knowledge needed but also understands how to develop solutions tailored specifically for your industry's needs. Let's transform your restaurant management with state-of-the-art AI. Skills: AI Integration, Deep Learning, Machine Learning (ML), Image Processing

@guptakishan805

As an experienced Full Stack Developer, I have specialized in AI-powered solutions and have a solid background in AI Model Development, Computer Vision, Deep Learning, Image Processing, and Machine Learning. This expertise aligns seamlessly with your project requirements of leveraging Roboflow and YOLOv12 to implement a computer vision system for your restaurant table management needs on the NVIDIA Jetson platform. Over the past 5+ years, I have helped numerous startups and businesses turn complex ideas into reliable digital products. Whether that be building custom web applications or creating SaaS platforms with impactful AI integrations - I am well-equipped to tackle your project. Moreover, my experience extends beyond just programming; I take a holistic approach to projects – understanding your goals deeply, suggesting improvements, and ensuring clear communication throughout the process. My ultimate aim is to not only provide excellent implementations of new technology but also to create value for your business in the long run. With these perspectives in mind and my skills at your disposal, we can build an intelligent restaurant table management system together that is both efficient and scaleable. Let's discuss how I can be a valuable asset to your project!

@nathanholmes

The interesting part of this project is not just detecting food items, but turning those detections into useful seating or table-management information on an edge device. Running the model directly on an NVIDIA Jetson also means inference speed, memory use, and reliable real-time processing need to be considered from the beginning. I’d build the pipeline around **Roboflow + YOLOv12**, training or fine-tuning the required food-item classes and then deploying an optimized model to the NVIDIA Jetson. I’d handle dataset preparation, annotation/validation, model training, inference integration, and Jetson-side optimisation, with detections mapped to the relevant table/seating logic. Where appropriate, I’d use hardware acceleration and an efficient inference pipeline to keep latency low and performance stable. I have experience with computer vision, object detection, YOLO-based systems, model deployment, and edge AI workflows. I can provide relevant examples and structure the work around dataset/model setup, Jetson deployment, detection validation, and final integration. Happy to review the food categories and table-management logic and define the implementation plan.

@PerryPrecise

Hi, I can build this restaurant table-management vision system around Roboflow + YOLOv12 and deploy it on NVIDIA Jetson. I’ll start by confirming the camera/feed and food-item labeling requirements, then prepare the dataset and augmentation pipeline, train and validate YOLOv12, and optimize the exported model for Jetson inference. I’ll integrate detections into a simple table-state workflow, include confidence and edge-case handling, and provide setup notes plus a testable demo. I can deliver the core implementation in 7 days for ₹25,000. Which Jetson model and camera stream format are you using, and do you already have labeled sample footage? Happy to keep the first milestone focused on a reliable end-to-end proof of concept.