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You will get Custom YOLOv8 Object Detection Model - Train & Deploy for Your Use Case
Project details
Transform your object detection needs into production-ready AI with custom YOLOv8 models. I build accurate, fast, deployment-ready computer vision systems.
What sets this apart:
✓ PROVEN RESULTS: 31× mAP improvement (0.014→0.445) in real projects, demonstrating model optimization expertise.
✓ PRODUCTION-READY: Models deployed with live web interfaces on HuggingFace or Streamlit, not just notebooks. Working applications, not just code.
✓ END-TO-END: Dataset prep, annotation, training, optimization, cloud deployment. Complete solution start to finish.
✓ EXPERTISE: M.Sc. Data Science & AI (Saarland University), RealPage Inc. experience, PyTorch and CV projects with real deployments.
✓ COMMUNICATION: Regular updates, detailed docs, post-delivery support. Technical concepts explained simply.
Perfect for: Product detection, quality control, defect detection, security, vehicle tracking, people counting, inventory management.
Deliverables: Trained model, source code, web app/API, performance metrics, documentation, deployment guide.
Let's build your detection solution!
What sets this apart:
✓ PROVEN RESULTS: 31× mAP improvement (0.014→0.445) in real projects, demonstrating model optimization expertise.
✓ PRODUCTION-READY: Models deployed with live web interfaces on HuggingFace or Streamlit, not just notebooks. Working applications, not just code.
✓ END-TO-END: Dataset prep, annotation, training, optimization, cloud deployment. Complete solution start to finish.
✓ EXPERTISE: M.Sc. Data Science & AI (Saarland University), RealPage Inc. experience, PyTorch and CV projects with real deployments.
✓ COMMUNICATION: Regular updates, detailed docs, post-delivery support. Technical concepts explained simply.
Perfect for: Product detection, quality control, defect detection, security, vehicle tracking, people counting, inventory management.
Deliverables: Trained model, source code, web app/API, performance metrics, documentation, deployment guide.
Let's build your detection solution!
Machine Learning Tools
Keras, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, SciPy, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$400
|
Standard
$750
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 10 days | 14 days | 21 days |
Number of Revisions | 2 | 3 | 5 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 5 | 7 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$100
Rush Delivery (50% faster)
+$150
Dataset Annotation (100 images)
+$200
REST API Development
+$250Frequently asked questions
About Yojaswi
ML Engineer | Computer Vision | PyTorch & YOLOv8 Expert
Saarbruecken, Germany - 10:22 am local time
🎓 EDUCATION & BACKGROUND
- M.Sc. in Data Science & Artificial Intelligence - Saarland University, Germany (Current)
- B.Tech in Computer Science (AI & ML Specialization) - 2024
- Professional experience at RealPage Inc. as Developer Analyst & Data Analyst
💻 WHAT I OFFER
🔹 Computer Vision & Deep Learning:
- Object Detection (YOLOv8, YOLO series)
- Semantic Segmentation (SegFormer, U-Net, DeepLab)
- Anomaly Detection (PatchCore, unsupervised methods)
- Image Classification & Recognition
- Real-time Video Processing
🔹 ML Model Development:
- Custom model training & fine-tuning
- Transfer learning & domain adaptation
- Model optimization for inference speed
- Hyperparameter tuning & experimentation
- Dataset preparation & augmentation
🔹 Deployment & Production:
- HuggingFace Spaces deployment
- Streamlit application development
- API development (FastAPI, Flask)
- Docker containerization
- Cloud deployment (AWS/GCP/Azure)
🔹 Technical Stack:
- Frameworks: PyTorch, TensorFlow, scikit-learn
- CV Libraries: OpenCV, Ultralytics, Albumentations
- Tools: Git, Jupyter, Google Colab, Weights & Biases
- Languages: Python, C++, SQL
📊 PROVEN RESULTS
✅ SegFormer Fog Segmentation: Achieved 60.6% mIoU (11% improvement) on ACDC dataset with optimized training pipeline
✅ YOLOv8 Pothole Detection: Improved mAP@50 by 31× (from 0.014 to 0.445) through transfer learning and custom data preparation
✅ PatchCore Anomaly Detection: Reached 98%+ AUROC on MVTec AD dataset for industrial defect detection
✅ Production Deployments: Multiple live ML applications on HuggingFace Spaces and Streamlit Cloud with interactive demos
🎯 WHY WORK WITH ME?
- I deliver production-ready code, not just notebooks
- Clear communication and regular updates throughout the project
- I explain technical concepts in simple terms
- Fast turnaround with attention to detail
- Experience working with real-world datasets and business requirements
🚀 READY TO BRING YOUR ML/CV PROJECT TO LIFE?
Whether you need a custom object detection model, image segmentation pipeline, anomaly detection system, or ML deployment solution, I can help you build it from scratch or improve existing systems.
Let's discuss your project requirements and create a solution that works!
📧 Available for both short-term tasks and long-term collaborations.
⏰ Typically respond within 2-4 hours during business hours.
Steps for completing your project
After purchasing the project, send requirements so Yojaswi can start the project.
Delivery time starts when Yojaswi receives requirements from you.
Yojaswi works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements Review & Project Planning
Review your requirements, confirm object classes and success metrics. Discuss project scope and timeline. Set up communication schedule for updates. Deliver project plan document with milestones and deliverables.
Dataset Preparation & Annotation
Organize training images, annotate with bounding boxes if needed. Apply data augmentation (rotation, flip, brightness). Split into train/validation/test sets (70/20/10). Deliver dataset statistics and sample annotations.