AI Vision System - YOLO Dataset Preparation (Only taking Proposals for phase 1)

@TheElitePro

Hi, 1. I understand Phase 1 is strictly focused on image scrubbing and defect classification—filtering unusable highway images and consistently grouping valid ones into 34–36 defined classes. 2. I have experience working with different datasets and structured labeling workflows. My background includes handling image data pipelines, preprocessing for ML models, and maintaining high consistency across classifications—critical for downstream YOLO training. 3. My approach: a) Define clear acceptance/rejection criteria (blur, no-defect, irrelevant frames) b) Perform batch-wise review to maintain consistency across similar images c) Use reference examples to standardize class boundaries and reduce ambiguity d) Flag edge cases early to avoid misclassification drift e) Maintain structured folder hierarchy aligned with class taxonomy f) Periodically self-audit samples to ensure labeling consistency 4. Turnaround: For an initial batch (size dependent), I can process efficiently while prioritizing accuracy—typically within 24–72 hours for moderate batches, with consistent throughput afterward. 5. Relevant experience: Image dataset preprocessing and classification for ML workflows Working with structured labeling systems and preparing data for training pipelines Strong focus on quality control to ensure reliable model performance I’m detail-oriented and understand that data quality at this stage directly impacts model accuracy. Happy to start with the test batch. Regards, Vishal

@quan9824

Hi, I have solid experience in computer vision data preparation, including image annotation and YOLO dataset creation. I’ve worked with multi-class object detection tasks (20+ classes) and understand how critical consistency and accuracy are for model performance. YOU CAN CHECK THEM ON MY PORTFOLIO. I can: • Annotate images using LabelMe with bounding boxes or polygons as required • Strictly follow your 34–36 class definitions using provided examples • Maintain consistent labeling across similar defects • Convert annotations to YOLO format with correct class IDs • Organize dataset into clean train/val structure with full validation I understand YOLO format (normalized coordinates, class indexing, matching image-label pairs) and will ensure no missing or incorrect annotations. I also perform quality checks before delivery to avoid inconsistencies. Turnaround: depends on volume, but I can handle structured batches efficiently while maintaining high accuracy. I’m comfortable working with large class systems and following strict labeling rules. I’m also happy to complete a test task and share similar CV/annotation work samples.

@hotanloc

Hi. Client. Thanks for your posting. Your project is just my project so I will always do my best to meet your all requirements. And please check my portfolio and reviews in Image generation using GAN projects. I have full experiences in IMAGE ANNOTATION and VIDEO FRAME ANNOTATION. I have full experiences in huge amount image data annotation whith image labeling too for training from ML/DL model. I have annotated huge amount data for traing model before. I am working more than 5 yesrs in this field. I can finish your task with high quality on time. If you give me your project, You can get best result with shortest time and best quality result. Please send me your message to discuss your project detail more...I am waiting your reply now. Thanks.

@tadinhkien

Greetings, Thank you for considering my application for this project. As an AI Engineer and Python Developer with over 8+ years of experience, I bring a wealth of knowledge and expertise in the field of Python, Deep Learning. I have carefully reviewed the project description and am eager to discuss your specific needs and requirements in more detail. My commitment is to provide dedicated support and consistent follow-up throughout the project's lifecycle. Please feel free to reach out to me to further discuss how I can contribute to the success of your project. Looking forward to the opportunity of working together. Best regards, KuroKien

@usmanbashir12100

Hi, I understand that Phase 1 is focused strictly on image scrubbing and classification, ensuring only high-quality, relevant images are retained and consistently grouped by defect type. My approach is systematic: I review images in batches using a defined checklist (quality, relevance, visibility of defects), then classify them based on your reference examples while maintaining strict consistency across similar cases. I also keep a structured folder system and naming convention to ensure everything is clean and ready for the next phase. I have experience working with large datasets and understand the importance of accuracy over speed, especially for AI training. I can deliver an initial batch quickly (typically within 24–48 hours depending on size) and maintain consistent quality throughout. Happy to complete the test task and get started.

@zahidhasta

Hi, I understand that Phase 1 is focused strictly on image scrubbing and pre-classification, where accuracy and consistency are critical before any annotation begins. My approach is to first define clear filtering rules for unusable images such as low quality, irrelevant content, or no visible defects. Then I create a structured review workflow where images are batch processed and grouped based on visual similarity and provided class references. I typically maintain a reference guide to ensure consistent classification across similar defect types and avoid class drift over large volumes. For large datasets, I combine manual review with semi-automated sorting strategies to speed up processing while keeping quality high. I also maintain clean folder structures and logs to track decisions, which helps later phases like annotation and YOLO conversion. For the initial batch, I can deliver within 1 to 2 days depending on size, with a focus on precision over speed. I’m ready to proceed with the test batch and align on classification standards before scaling further. Best regards Zahid Hassan

@Muhammadzeesha59

I recently embarked on my journey into the vibrant world of Computer Vision and have gained substantial expertise since. I've been immersed in projects that bear semblance to yours, supervising and organizing mammoth-scale image datasets, and ensuring data quality bears utmost importance in these Create AI projects. In conjunction with my razor-sharp attention to detail, I'm well-versed with the usage of LabelMe for annotation. My commitment is to ensure consistency and accuracy across all phases of the project, which aligns perfectly with your requirements for this AI Vision System. This project necessitates adherence to structured instructions - a skill I have cultivated throughout my career. Moreover, familiarity with YOLO format isn't an alien territory for me. Given the centrality of Phase 1 to the overall success of the project, I assure you that even though I value accuracy over speed, it won't compromise on timely deliveries. I understand that your dataset is vast and handling it would certainly require some timescales, thus you can expect me to complete an initial batch methodically and promptly. My proficiency spans much more than just image annotation; However, I believe that Phase 1 will culminate in exceptional outcomes that will reflect my finesse in managing large datasets.

@ABDOKAMA

Hi, I have extensive experience in computer vision and have completed several key projects in the field. My work includes animal detection and identification (such as identifying tigers), license plate detection and recognition, and facial hair segmentation. I’ve worked on many other projects as well and would be very happy to collaborate with you. Thanks!

@Shrutibimpl

Hello, I will establish a systematic workflow to review and filter your large collection of highway images. I will first remove any low quality or irrelevant files to keep the dataset focused and efficient. I will then categorize the useful images into distinct defect groups following your classification standards. My process focuses on high consistency to ensure that similar defects are always grouped together correctly. This structured approach will provide a clean foundation for the subsequent training phases. 1) What are the specific defect categories you have defined so far? 2) In what format or platform are the images currently stored? 3) Do you have a small set of labeled examples for me to use as a reference? Thanks, Bharat

@ArtisticQueens

Hi There, I’ve carefully reviewed your requirements "Title: Image Annotation Specialist (LabelMe) + YOLO Dataset Preparation (34 to 36 Classes) Descript......" for “AI Vision System - YOLO Dataset Preparation”. With 10+ years of experience, I bring a structured and creative approach to projects involving Image Processing, Object Detection, Computer Vision and YOLO. Portfolio: https://www.freelancer.com/u/ArtisticQueens

@Lazarone1

Hi, that’s great to hear! Your project closely aligns with one I recently worked. In that project, I built a fully structured multi-class defect detection dataset using LabelMe, YOLO formatting tools, and custom validation scripts with precise labeling rules, class-matching workflows, and dataset structuring for training. For your AI-based visual inspection system, I can help annotate all images with consistent bounding boxes or polygons, maintain strict labeling conventions across 34-36 classes, and deliver a clean YOLO-ready dataset with matching label files for both train and val splits. I ensure accuracy, consistency, and proper conversion from LabelMe to YOLO, including quality checks before submission. I’d be glad to connect and share my experience in more detail over chat. Thank you. Best regards, Lazar

@demivision

Hello, Thank you for sharing the detailed requirements for your AI visual inspection system. At DemiVision, LLC, we have extensive experience in image annotation, multi-class object detection datasets, and dataset preparation for YOLO training. We understand the importance of accuracy, consistency, and strict adherence to class definitions, especially when working with 34 to 36 defect classes. Our team is proficient with LabelMe and familiar with both bounding box and polygon annotations. We’ve successfully managed projects involving large class systems and complex labeling conventions, ensuring datasets are always properly structured and validated prior to delivery. We follow a rigorous quality control process, including cross-verification and dataset integrity checks, to guarantee every image and label is correct and ready for YOLO training. We are confident in handling your class structure, converting annotations to YOLO format, and organizing the dataset for seamless model training. We can provide examples of similar annotation work and confirm our understanding of the YOLO labeling system and requirements. We look forward to contributing to the success of your AI project and are ready to complete your test task to demonstrate our capabilities.

@DngPhgNam

⭐⭐⭐⭐⭐ ✅Hi there, hope you are doing well! I have completed similar projects involving multi-class image annotation for object detection, where I used LabelMe to create consistent bounding boxes and polygons that integrated seamlessly with YOLO training pipelines. The key to success in this project lies in meticulous adherence to the provided class definitions and labeling conventions to ensure dataset quality and model accuracy. Approach: ⭕ I will first thoroughly review the 34 to 36 class definitions and samples. ⭕ Annotate images precisely using LabelMe, ensuring consistency across all defect classes. ⭕ Convert annotations accurately into YOLO format with correct class IDs. ⭕ Organize the dataset into the specified train/ and val/ folders. ⭕ Perform quality control checks to avoid errors and missing labels before delivery. ❓ Could you please clarify if there are any preferred settings or templates in LabelMe you want used? ❓ Do you expect any integration with existing annotation tools or pipelines? ❓ How many images are in the initial batch? I am confident that my detailed-oriented experience with multi-class labeling and YOLO dataset preparation will deliver a clean, ready-to-train dataset that fits perfectly into your AI vision system pipeline. Looking forward to your response. Best regards, Nam

@ShaikhAneesa

Hello, I have carefully reviewed the requirements for the AI Vision System project, specifically the need for Image Annotation Specialist using LabelMe and YOLO Dataset Preparation for 34 to 36 classes. Let's chat and discuss it further. To handle your project, I will start with utilizing LabelMe for precise image annotation, drawing bounding boxes or polygons, and labeling objects according to the specified class list. I will then convert these annotations into YOLO format, ensuring correct class IDs and structure, and organizing the dataset into train/val directories while maintaining consistency and accuracy throughout. The deliverables for this project will include a fully labeled dataset, YOLO-formatted label files (.txt), and a well-structured folder ready for training. Before signing-off my bid, I would like to ask a question, i.e., how many images are included in the initial batch for scrubbing and separation into classes? Warm Regards, Aneesa.

@Yusrazubair1000

Hi There, I have carefully reviewed your requirements for the Image Annotation Specialist position and am confident that my experience in image annotation and dataset preparation aligns perfectly with your needs. My background in working with similar tools and formats makes me well-suited for this project. ### Questions for Clarification: 1. Are there any specific tools you prefer for the annotation process, or is LabelMe the only option? 2. How many images are in the initial batch, and what is the expected turnaround time for that batch? 3. Is there a specified format for the clean folder structure you would like for the output? ### Why Choose Me? - Extensive experience with image annotation tools such as LabelMe and LabelImg. - Proven track record with multi-class object detection datasets, including experience handling 20+ class labeling systems. - Strong attention to detail and commitment to following structured instructions, ensuring high accuracy and consistency. - Familiarity with the YOLO dataset format and the ability to convert LabelMe annotations accordingly. ### Availability: I am available from 9 AM - 9 PM Eastern Time and can dedicate full-time hours to ensure timely delivery of high-quality work. I look forward to discussing this project further and am happy to provide a sample of my previous annotation work upon your request. Best regards, Syeda Yusra Zubair

@huonghy89

Hi, How are you? Very happy to bid for your project because my skills are fitted in your project. I am 8 years of experience in Machine learning, deep learning, OCR, Image processing and computer vision. I am very familiar with openCV, MTCNN & Facenet, YOLO, GPU, Mediapipe, Generative Adersarial Network, openAI, CNN, RNN, GAN, LSTM, SVM, reinforement learning, opencv, Pytorch, tensorflow, keras, tesarret Pandas, sklearn, numpy, matplotlib, seaborn and so on. I have made the apps for ANPR/ALPR, cancer detection, image classification and recognition, object detection and tracking, sentiment classification, license recognition, ID card recognition, hand gesture recognition, face emotion recognition, face swapping, virtual cloth trying, template matching. I can do your project perfectly. If you send the message , we can discuss the project more. Thanks.

@jvy7802

Hi, This is Jorge from IT GLOBAL SOLUTION LLC, based in the U.S. I’ve worked on computer vision pipelines where annotation quality directly impacts model performance, especially with multi-class YOLO datasets—so I understand how critical consistency and structure are for this kind of task. I’m comfortable using tools like LabelMe and handling both bounding boxes and polygon annotations depending on object type. For datasets with 30+ classes, I follow a strict internal validation approach to ensure labels remain consistent across similar defect types and no class drift occurs over time. For YOLO preparation, I’ll handle clean conversion from LabelMe JSON to YOLO format, ensuring correct class IDs, normalized coordinates, and a properly structured dataset (train/val split, matched image-label pairs, no missing files). I also run validation checks to catch empty annotations, mismatches, or formatting issues before delivery. Accuracy is my priority—I take time to cross-check edge cases and ambiguous samples against your reference examples to maintain labeling consistency across the dataset. I’m fully comfortable working with a 34–36 class system and following strict labeling conventions. Happy to provide sample work and align on your class definitions before starting. Let’s connect and get this dataset prepared correctly. Best, Jorge

@shafeeqm6

hey i think you would like to read this The precision required in maintaining consistent labels across 34 to 36 defect classes is crucial for effective YOLO training. With my solid experience in LabelMe annotation and YOLO dataset preparation, I will meticulously follow your class definitions and naming conventions to ensure clean, uniform annotations. My focus on detail and thorough quality control means your dataset will be reliable for training your AI-based inspection system, minimizing errors and maximizing accuracy. Deliverables: - Complete annotated dataset in LabelMe format - YOLO-formatted .txt label files with correct class IDs - Organized train/val directories - Validation report on annotation consistency and accuracy I would like to discuss more about the project. You lose nothing. If milestones are created, your payment will be fully protected and you can use this message as proof of a full refund guarantee. Kind Regards Shafeeq

@ayesha86664

Hi there! You are building a multi-class visual inspection dataset, and the real challenge is keeping all 34–36 defect classes consistent across hundreds of images—that is exactly where most projects lose accuracy. I recently prepared a YOLO-ready dataset with 28 defect classes for an industrial inspection AI, where every annotation passed strict quality checks and model training improved detection accuracy by 18%. I handled LabelMe annotation, class consistency, and conversion to YOLO format seamlessly. I will annotate your images using LabelMe, strictly follow your class definitions, convert everything into YOLO format, and organize the dataset with full QC so it’s ready for training without errors. Check our work: https://www.freelancer.com/u/ayesha86664 Do you want bounding boxes, polygons, or a mix depending on the defect type for this dataset? I am ready to start — just say the word. Best Regards, Ayesha