You will get Fine-Tune SegFormer & RAAGR2-Net for Custom Semantic Segmentation models
Project details
This project focuses on building a complete image segmentation pipeline from scratch using advanced deep learning architectures such as SegFormer and RAG-based (RAG2Net) models. I have implemented these models end-to-end in PyTorch, including data preprocessing, model design, training, and evaluation on custom datasets.
My work involves training segmentation models specifically for medical imaging applications, where accuracy and precision are critical. I use evaluation metrics such as IoU and Dice score to ensure reliable performance and optimize the models accordingly.
What sets this project apart is the ability to develop and train these architectures from scratch for custom datasets, along with generating clear visual outputs (input image, ground truth, and prediction) to demonstrate model performance. These approaches are particularly effective in medical domains such as tumor or organ segmentation.
I am confident in implementing and adapting both SegFormer and RAG-based models using PyTorch, tailored to specific client requirements, ensuring high-quality results suitable for research or real-world applications.
My work involves training segmentation models specifically for medical imaging applications, where accuracy and precision are critical. I use evaluation metrics such as IoU and Dice score to ensure reliable performance and optimize the models accordingly.
What sets this project apart is the ability to develop and train these architectures from scratch for custom datasets, along with generating clear visual outputs (input image, ground truth, and prediction) to demonstrate model performance. These approaches are particularly effective in medical domains such as tumor or organ segmentation.
I am confident in implementing and adapting both SegFormer and RAG-based models using PyTorch, tailored to specific client requirements, ensuring high-quality results suitable for research or real-world applications.
AI Development Type
Deep Learning, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Amazon SageMaker, Keras, NVIDIA AI Platform, OpenCV, PyTorch, TensorFlowWhat's included
| Service Tiers |
Starter
$350
|
Standard
$550
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 15 days | 15 days | 10 days |
Number of Revisions | 2 | 3 | 10 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | ||
Model Documentation | |||
Ontology | - | - | |
Source Code | - | ||
Taxonomy | - | - |
Optional add-ons
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Fast Delivery
+$250 - $150About VishnuVardhan
AI/ML Engineer | Computer Vision & NLP Specialist
Hyderabad, India - 1:06 pm local time
I build high-impact AI systems for real-world business outcomes not prototypes, demos, or academic experiments. My work ships at scale in high-stakes domains like healthcare, automotive, and customer support, with a focus on medical imaging, RAG/LLMs, and production-grade integration.
PROVEN RESULTS IN PRODUCTION:
98% Dice score on brain tumor segmentation (MRI) using U-Net, RAAGR2-Net, DeepLabV3, and SegFormer—from scratch in PyTorch (benchmarking for clinical accuracy).
95% accuracy car damage classifier deployed on VROOM Cars (production environment).
92% top-1 accuracy food classifier across 101 categories via ViT fine-tuning.
RAG chatbot that reduced support workload by 27% using PDF knowledge bases, ChromaDB, and FastAPI.
SERVICES
✅ AI Data Annotation & Labeling
MRI, video, and image annotation for computer vision pipelines — bounding boxes, semantic masks, and clinical labeling using LabelStudio, Roboflow, and CVAT. Medical-grade accuracy for production pipelines.
✅ Generative AI & LLM Engineering
Custom LLM fine-tuning, prompt engineering, RAG systems, and multimodal AI pipelines — engineered for business use, not experimentation.
✅ AI Integration & Deployment
Embedding AI into existing business workflows via FastAPI backends, REST APIs, and end-to-end deployment on AWS and Docker. Built for seamless adoption in live systems.
✅ Medical AI & Precision Segmentation
Tumor and lesion segmentation using U-Net, RAAGR2-Net, SegFormer, and DeepLabV3 — custom-trained for clinical accuracy in medical imaging workflows.
✅ Machine Learning & Deep Learning
Custom model architecture, supervised and unsupervised training, transfer learning, and model optimization using PyTorch and TensorFlow.
✅ Full POC-to-Production
From prototype to deployed product — PyTorch/TensorFlow → Docker/AWS. End-to-end delivery with full handoff support.
𝗟𝗘𝗧'𝗦 𝗖𝗢𝗟𝗟𝗔𝗕𝗢𝗥𝗔𝗧𝗘:
✅ Every deliverable includes 10 days of post-delivery support — no one-off fixes
✅ Available 50+ hours/week with a 4-hour response guarantee
✅ All systems are production-deployed, not notebook experiments
Send your project details and I will respond within 4 hours with a clear technical approach and timeline.
Steps for completing your project
After purchasing the project, send requirements so VishnuVardhan can start the project.
Delivery time starts when VishnuVardhan receives requirements from you.
VishnuVardhan works on your project following the steps below.
Revisions may occur after the delivery date.
data preprocessing