You will get Fast labeling / Annotation for Object Detection Datasets
Project details
You’ll get a high-quality, training-ready labeled dataset without spending weeks drawing boxes by hand. This project assumes you already have 5–10% of your dataset pre-annotated (or are willing to create that). If you don’t have any labels yet, please visit a tool like Roboflow.com, create a project, and do a small, representative annotation pass on your data. That labeled subset will be used as the training set for the model.
From there, I take over: I train a state-of-the-art detection model on your annotated subset, then use that model to generate pseudo-labels for the rest of your dataset and correct a portion of the predictions to calibrate quality (typically only ~10–20% need manual fixes). You’ll receive your dataset in your preferred format (e.g. YOLO / COCO), plus a short write-up explaining the pipeline, expected label quality, and recommended next steps.
From there, I take over: I train a state-of-the-art detection model on your annotated subset, then use that model to generate pseudo-labels for the rest of your dataset and correct a portion of the predictions to calibrate quality (typically only ~10–20% need manual fixes). You’ll receive your dataset in your preferred format (e.g. YOLO / COCO), plus a short write-up explaining the pipeline, expected label quality, and recommended next steps.
AI Development Type
Deep LearningAI Tools
NVIDIA AI Platform, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$50
|
Standard
$100
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
Frequently asked questions
About David
Senior AI Engineer | Deep Learning | Computer Vision | NVIDIA A100GPU
Goeteborg, Sweden - 11:46 pm local time
Welcome! I'm here to help you achieve your AI goals – in a precise, quick and clear way. Whether you want a custom AI model, optimize your current AI pipeline or explore GPU possibilities, my main goal is to ensure your satisfaction by delivering robust solutions.
I’m a Deep Learning engineer (4+ years) specializing in custom AI models trained on your own data, with a strong focus on PyTorch, computer vision, and NLP (Natural Language Processing). I work directly on an NVIDIA A100–datacenter class GPU, so I can handle serious experiments, larger models, and fast iterations.
What I can help you with:
— Design and train custom models (vision, text, or tabular) on your proprietary dataset
— Fine-tune existing foundation models (for example, Vision Transformers and other transformer architectures) for your specific use cases.
— Build end-to-end training pipelines: data preprocessing, training, evaluation, and reporting
— Improve or refactor existing code for better performance, stability, and clarity
— Provide research-grade prototypes that can be turned into production systems
Why work with me?
— Strong research + engineering blend: I’m comfortable reading papers, implementing new methods, and turning them into robust, readable code.
— Proven real-world impact: For Rörstrand Museum in Sweden, I helped build an AI prototype that recognizes and dates ceramic objects from images, so staff can catalogue pieces faster, answer visitor questions more accurately, and make more of the collection available digitally.
— Serious compute, faster feedback: Access to an NVIDIA A100 GPU means quicker experiments, more iteration, and better models within the same budget.
— Clear communication: I explain trade–offs in plain language and keep you involved in key decisions, so you always know what you’re getting and why.
How I work:
— I start by clarifying your goal: what does “success” look like in business terms (not just accuracy numbers)?
— I review your data, propose a concrete plan (model choice, metrics, timeline), and break it into clear milestones.
— You get transparent updates, clean and well-documented code, and a short summary of results and next steps.
Tech stack:
— Languages: Python
— Core frameworks: PyTorch, TensorFlow, CUDA, Triton
— Domains: computer vision, NLP, classification, detection, recommendation, anomaly detection
— Compute: NVIDIA A100 GPU for training and experimentation
If you have a specific dataset or idea and want to see what is actually possible with modern deep learning, send me a short description of your use case and data, and I’ll let you know how I can help.
Steps for completing your project
After purchasing the project, send requirements so David can start the project.
Delivery time starts when David receives requirements from you.
David works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff & data review
You share your use case, classes to label, and a link or sample of your dataset. I check data quality and confirm that pseudo-labeling is suitable for your case.
Manual annotation of a small subset
I manually annotate an initial subset of your images (e.g. up to ~500 annotations). If this proves insufficient for a reliable model, I’ll tell you how many more labeled examples we need before proceeding.