Image Bounding Box Annotation

@Osama141

Hey! I've worked on a number of projects similar to this one. I have a lot of experience and knowledge in this field. My knowledge of business also gives me an advantage in this situation. Looking forward for the opportunity. Thanks

@fozansani05

Hi, I am Fozan. I can precisely annotate, create bounding boxes that touch the edges of the object, and categorize them into two classes. I can easily do 150-200 images in a day, but it will also depend on the complexity of the images. We will discuss that as soon as we start working on it. Consider me for your project. Waiting for your response. Regards and thanks, Fozan.

@Thabs9

Hi TM Mohale, I understand the scope of your Image Bounding Box Annotation project and its critical importance in generating accurately annotated data for your training pipeline. With my expertise in XML, Data Processing, and Object Detection, I guarantee precise bounding boxes around "People" and "Vehicles". Ensuring consistent accuracies across annotations and proficiency in tools like LabelImg demonstrates my attention to detail. With your budget in mind and a commitment to quality, I can efficiently handle [number of images per day] images, using your preferred tool. Let's start collaborating to deliver quality results while you enjoy a reduced rate. Looking forward to our potential partnership. Regards, Thabo

@Lerikus

Our background makes us well-suited to handle this project effectively, I’ve successfully delivered work comparable to what you need. Your emphasis on producing clean, professional, and user-friendly bounding box annotations for “people” and “vehicles” shows a commitment to accuracy and seamless integration with your training pipeline. We excel in precise, automated image annotation using tools like LabelImg and CVAT to ensure well-aligned, consistent bounding boxes. While our company is new to the freelancer platform we are not new to the industry, we have a wide range of experience and expertise in delivering high-quality image annotation projects with uniform JSON, XML, or YOLO txt exports. I’m available to discuss the project in more detail whenever you are ready. Regards, Lerikus

@victorodunsi44

Hello, I’d be happy to support your annotation project. I have solid experience creating high-precision bounding boxes using LabelImg, Roboflow, and similar tools, and I’ve annotated both microscopy/medical images and general object-detection datasets for production ML systems. Relevant Experience PlasmoScan (Malaria Detection): Annotated hundreds of high-res microscopy images in LabelImg, producing tight YOLO-format boxes for parasite vs. non-parasite classes. Required extremely accurate box placement due to small, dense objects. Roboflow Pipelines: Used Roboflow for dataset organization, preprocessing, class management, and augmentation workflows. YOLOv8/YOLOv11 Training: Strong understanding of how poor labeling affects model performance, so I prioritize accuracy and consistency. How I’ll Deliver Careful manual review of every image Tight, correctly aligned boxes for “person” and “vehicle” Export in YOLO txt, Pascal VOC XML, or JSON All original images untouched Clean annotation files (per-image or consolidated) A brief spot-check summary with counts and edge cases Capacity & Tools 60–100 images/day, adjusted based on complexity Preferred tools: LabelImg (YOLO/VOC) and Roboflow for dataset QA and versioning If this matches what you need, I can start immediately, and we can begin with a small test batch to confirm quality.

@Kruti229

Hey, I have worked with data annotation project before not image but had training for bounding box. Also I have experience in AI projects like classification, detection model building. Would like to work with you.

@pkushagra22

I have vast experience in data annotations. I can deliver this project with most accurate annotations along with other requirements.

@nairab1

Hello! I’d be happy to support your dataset annotation project with precise, high-quality bounding box labeling. I have practical experience annotating computer vision datasets using industry-standard tools like LabelImg, CVAT, and Roboflow, and I fully understand how important tight and accurate annotation is for model performance. I pay very close attention to alignment, object boundaries, occlusions, and edge cases to avoid missed detections or loose boxes. What I Will Deliver Original images untouched Consistent annotation files — I can provide YOLO .txt, PASCAL VOC .xml, or COCO/JSON (your preference) Class mapping strictly to “person” and “vehicle” Spot-check summary report: • Total images processed • Special/uncertain cases flagged • Notes on any anomalies in the dataset Quality & Workflow I will review each image carefully, ensuring tight, well-aligned boxes Quick communication about any image ambiguities Quality is the top priority before speed — no missed objects, no sloppy annotations Productivity I can comfortably annotate 120–180 images per day (depending on object density per image). If your dataset size is larger, I can scale accordingly while maintaining accuracy. Tools & Pipeline Compatibility Preferred tools: CVAT or Roboflow (easier collaboration, export in multiple formats) But I am flexible and can match any format required for your training pipeline.

@bagongisdead

I have nearly six years of experience with projects related to bounding boxes, classification, segmentation labeling, and the like. Using CVAT, Labelit, Dataplus, and Labelme, I have carried out projects related to box labeling using these platforms. Is this for autonomous vehicles? If so, does it involve projects related to video annotation or Lidar? You can contact me for further discussion regarding your project, and I am ready to join your team immediately if selected.

@AlexMS16

Hello, i have experience annotating bounding box using roboflow before. Feel free to reach out if you'd like to discuss the project further. Thank You.

@ahmeda297

I’m interested in supporting your image annotation task and would be happy to be your reliable partner for this project. I have strong experience with high-quality bounding box annotation for People and Vehicles, with a strong focus on precision and consistency suitable for direct use in training pipelines. I can comfortably annotate around 1,000 images per day manually using tools such as CVAT, LabelImg, or Roboflow, ensuring tight, well-aligned bounding boxes and careful review of edge cases. In addition, I can accelerate the process by leveraging SAM3 to pre-generate annotations and then manually verify and refine them, allowing the entire dataset to be completed in one day while maintaining high quality. I can deliver annotations in YOLO format (preferred for training efficiency) or export to JSON/XML if needed, with uniform formatting across all files. Along with the annotations, I will provide a spot-check report summarizing the total number of images processed and any notable edge cases encountered. Quality is my top priority, and I understand the importance of accurate labels to avoid costly rework. I’m ready to start immediately and can adapt to your preferred workflow. Looking forward to working together. Kind regards, Ahmed Asem Computer Vision Engineer

@SumitJethuri

I have solid experience annotating object detection datasets and can deliver precise, tightly aligned bounding boxes for people and vehicles with a strong focus on quality. I manually review every image to avoid missed objects or sloppy boxes and export annotations in a consistent format (YOLO preferred, with COCO JSON or Pascal VOC XML if needed). I actually have trained models too, so i know how to maintain accuracy. I work comfortably with tools like CVAT and LabelImg, provide a brief spot-check summary after delivery, and can handle 300-500 images per day depending on complexity while maintaining accuracy. As i am unaware of the image quantity. I can not project the timeline.

@vinayakc44

Detail-oriented Data Annotation & QA Specialist with 3 years of experience in labeling and validating LiDAR, image, and video data for AI/ML model training. Skilled in annotation (2D/3D bounding boxes, segmentation, panoptic labeling) and quality assurance, ensuring accuracy, consistency, and compliance with project guidelines. Adept at supporting autonomous driving datasets, road infrastructure mapping, and traffic scene understanding projects.