You will get Fine-tune a Vision Transformer (ViT) for Image Classification
Project details
I fine-tune Vision Transformers (ViT) using Hugging Face and PyTorch to automatically identify and categorise images and deliver a working web interface so your clients or team can upload a photo and instantly see the predicted class with a confidence score. No ML background needed on your end to use it.
This project is built on a pipeline I developed to achieve 92% accuracy on Food-101, a challenging 101-category benchmark. The same architecture adapts directly to other domains: manufacturing defect detection, medical scan , e-commerce product categorisation, or any custom image dataset you bring. I review your data first, handle preprocessing and augmentation, fine-tune the model, and produce a full evaluation report with accuracy, precision, recall, and a confusion matrix — so you know exactly what you are getting before final delivery.
What sets this project apart is the combination of a production-ready model and a usable interface in a single package. Most ML freelancers deliver a Jupyter notebook. I deliver clean, documented source code, a web app you can run immediately, and a README so your team can retrain on new data without coming back to me
This project is built on a pipeline I developed to achieve 92% accuracy on Food-101, a challenging 101-category benchmark. The same architecture adapts directly to other domains: manufacturing defect detection, medical scan , e-commerce product categorisation, or any custom image dataset you bring. I review your data first, handle preprocessing and augmentation, fine-tune the model, and produce a full evaluation report with accuracy, precision, recall, and a confusion matrix — so you know exactly what you are getting before final delivery.
What sets this project apart is the combination of a production-ready model and a usable interface in a single package. Most ML freelancers deliver a Jupyter notebook. I deliver clean, documented source code, a web app you can run immediately, and a README so your team can retrain on new data without coming back to me
Machine Learning Tools
Amazon SageMaker, Python, scikit-learnWhat's included
| Service Tiers |
Starter
$150
|
Standard
$350
|
Advanced
$700
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 16 days |
Number of Revisions | 1 | 3 | 5 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 2 | 10 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$400 - $500About VishnuVardhan
AI/ML Engineer | Computer Vision & NLP Specialist
Hyderabad, India - 8:27 am 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.
Dataset review
Data preprocessing and augmentation