5,000 Image Bounding-Box Annotation

@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

@mobasherreza10

Hi there, As an agency specialized in AI and image processing, I can confidently say that Aesthetic Logic is the perfect fit for your image bounding-box annotation project. Our team of expert data annotators and deep learning engineers are proficient in using mainstream tools such as LabelImg, CVAT, and Supervisely to deliver high-quality results. We have a fine appreciation for your dataset's importance to an accurate machine learning pipeline since we implement similar models on a regular basis. Our meticulous approach to quality control guarantees your satisfaction. We will deliver 5,000 dataset files with pixel-perfect bounding boxes in YOLO format, fully aligned with your detailed annotation guidelines. Moreover, our commitment to transparency includes delivering a log that records our daily progress, ensuring you're informed every step of the way. Best regards, Mobasher Reza

@glennw6

Hello, I’m an experienced Computer Vision Data Annotator with hands-on experience using CVAT, Label Studio, and Roboflow for object detection datasets. I can annotate all 5,000 images with tightly fitted, consistent bounding boxes while following your guidelines precisely. I pay close attention to object boundaries, missed objects, duplicate annotations, correct class labels, and overall dataset consistency. I prefer **YOLO format** for the final export due to its compatibility with modern object-detection pipelines, but I can also deliver COCO JSON or Pascal VOC if preferred. My workflow will include: • Reviewing your guidelines and labeled examples • Completing an initial batch for quality verification • Consistent annotation and daily progress tracking • Ongoing QA to minimize errors • Final validation and export • Preserving your original folder structure Once I review the guidelines and sample images, I can confirm the exact turnaround time. I’m ready to start immediately and can provide a small sample first if required. Best regards, Glenn W.

@kalanas9

I don't just label images; I build the end-to-end Computer Vision engines (like YOLOv8) that consume them. Think of this dataset as the blueprint for your machine learning factory. If the blueprint is sloppy, the engine misfires. Because I train these models myself, I know exactly why your ≤2px tolerance is non-negotiable. Here is my 1-2-3 workflow to guarantee your dataset passes the 5% QA spot check flawlessly: 1. Tooling & Alignment I will use CVAT for its robust sub-pixel zoom control. I will start with a 20-image test batch based on your guidelines to ensure our expectations align perfectly before scaling up. 2. The Factory Line I will systematically process the 5,000 images, providing a brief daily progress log. Drawing on my past professional annotation experience, my workflow guarantees zero duplicates, missed objects, or mislabeled files. 3. Quality Control & Delivery I conduct my own internal spot-checks before exporting. I prefer delivering in the YOLO format, but can provide COCO JSON if you prefer. Everything will be delivered in a compressed archive mirroring your exact folder structure. I can turn around the full 5,000-image set in 10-12 days once guidelines are set. Ready to review your class list. Best regards, Kalana Systems Integrator

@malika335

5,000 IMAGE BOUNDING-BOX ANNOTATION Hi there! Accurately annotating your collection of 5,000 images with tightly-fitted bounding boxes (≤ 2 px tolerance) for your machine-learning pipeline—ensuring zero missed objects, strict adherence to your guidelines, and identical folder structure retention—is right in my wheelhouse. I regularly work on Computer Vision datasets, image processing, and AI media workflows, so feel free to check my profile portfolio. To answer your specific requirements directly: Export Format & Tools: I prefer using CVAT or LabelImg and can seamlessly deliver the final dataset in YOLO or COCO JSON format, depending on what feeds best into your specific ML pipeline. Turnaround Time: For 5,000 single-object images, I can complete and deliver the full set within 5 to 7 days. Tracking & Quality: I will provide the required daily progress log (image count per day) and ensure the dataset is thoroughly reviewed to easily pass your 5% random manual spot check. Before we get started, could you share the small set of fully-labeled examples so I can perfectly calibrate my bounding-box placement to your exact expectations? Once you share the guidelines and class list, I can map out everything cleanly and begin the daily annotation batches step-by-step. Looking forward to working together! Best regards, Ahmad Hassan

@nandezgarcia

Hi, I can annotate your 5,000 images accurately using Python-based tools and computer vision workflows. I will use CVAT or LabelImg, export to YOLO/COCO/Pascal VOC as you prefer, maintain tight bounding boxes within 2 px tolerance, and provide daily progress logs. I have experience with image datasets, YOLO and Python automation for annotation quality checks. Ready to start immediately with the guidelines and sample files.

@AnchelHuman

⚠️ IF YOU’RE NOT HAPPY, YOU DON’T PAY ⚠️ I have extensive experience in image annotation, having completed similar projects requiring precise bounding box placement for machine learning applications. My attention to detail ensures that each annotation meets your strict criteria for accuracy and consistency. I understand the importance of delivering a clean dataset that feeds directly into your machine-learning pipeline. I will utilize LabelImg or CVAT, ensuring the final output is in COCO JSON format, as it is widely used and compatible with most ML frameworks. I will maintain a daily progress log to keep you informed. I am currently building my reputation on Freelancer, so I’m offering a discounted introductory rate while maintaining the same professional standard I’ve delivered throughout my career. Let’s discuss how I can promptly meet your needs. Regards, Anchel

@KenTsaiTW

I can annotate the 5,000 images with consistent bounding boxes using your class definitions and preferred tool. I will begin with a small calibration batch, check label coverage, box tightness, class consistency, and empty or ambiguous cases, then run a second QA sample before each delivery. Please share the annotation format, class guide, and sample images.

@shanks2808

Hi, I specialize in computer vision annotation - bounding boxes and segmentation for ML training pipelines - so this fits directly within my skill set. I'll be working in CVAT for this - it's my go-to tool for bounding-box work and handles export to YOLO, COCO JSON, or Pascal VOC cleanly, whichever format you prefer. Once I have your class list and guidelines, I'll annotate a small batch first (matched against your fully-labeled examples) so you can confirm my accuracy before I commit to the full 5,000. I'll hold to your acceptance criteria strictly: tight boxes within 2px tolerance, no missed or duplicate objects, and consistent labeling throughout - built to pass your 5% spot check without corrections. Deliverables as requested: complete annotation files in your chosen format, a daily progress log (images completed per day), and a final compressed archive matching your original folder structure exactly. Turnaround: I can complete the full 5,000 images in 15-18 days once guidelines and the sample set are shared, working consistently through daily batches. Happy to start immediately. Which export format do you prefer - I'll get started as soon as you confirm. Best, Mukul