You will get a custom Vision-Language model (MOT,VQA etc ) & architectural engineering
Project details
Off-the-shelf Vision-Language Models (VLMs) fail on specialized, real-world data. General models suffer from domain gaps, vocabulary mismatches, and visual noise, causing the AI to make wrong associations between text and images.
I do not use generic templates or basic wrappers. I provide expert machine learning consulting, design custom VLM architectures from scratch, and engineer advanced multimodal systems tailored to your proprietary data.
What this project delivers:
• Custom Architecture Design: Modifying internal layers using smart weight initialization. This protects core foundation knowledge while resetting specific layers to adapt to your unique data.
• Optimized Cross-Modal Alignment: Re-engineering the attention layers where text and images meet. This fixes reasoning errors and guarantees accurate text-to-visual grounding, even with limited training data.
• Visual AI Transparency (XAI): Integrating custom diagnostic tools like attention heatmaps. This lets you see exactly what the AI looks at when making decisions, making your system safe and auditable.
I do not use generic templates or basic wrappers. I provide expert machine learning consulting, design custom VLM architectures from scratch, and engineer advanced multimodal systems tailored to your proprietary data.
What this project delivers:
• Custom Architecture Design: Modifying internal layers using smart weight initialization. This protects core foundation knowledge while resetting specific layers to adapt to your unique data.
• Optimized Cross-Modal Alignment: Re-engineering the attention layers where text and images meet. This fixes reasoning errors and guarantees accurate text-to-visual grounding, even with limited training data.
• Visual AI Transparency (XAI): Integrating custom diagnostic tools like attention heatmaps. This lets you see exactly what the AI looks at when making decisions, making your system safe and auditable.
AI Development Type
Deep Learning, Model TuningAI Tools
Keras, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$20
|
Standard
$60
|
Advanced
$120
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 7 days |
Number of Revisions | 2 | 3 | 2 |
AI Model Integration | - | ||
Detailed Code Comments | - | - | |
Knowledge Graph | - | - | - |
Model Documentation | - | - | |
Ontology | - | - | - |
Source Code | - | ||
Taxonomy | - | - | - |
About Usman
AI-engineer , computer vison expert
Lahore, Pakistan - 2:39 pm local time
solve real, specific problems — not generic demos.
My focus areas:
- Medical image analysis with explainable AI (heatmaps,
attention maps, saliency overlays)
- Vision-Language Models (VLMs) — fine-tuning on custom
datasets and domain-specific tasks
- Language-guided multi-object tracking
- OCR and document intelligence pipelines
- Object detection and segmentation
I work with PyTorch, OpenCV, HuggingFace, and Python.
I deliver clean, documented, production-ready code that
you fully own.
Current project work includes:
- Explainable AI pipeline for gynecological condition
detection from medical imaging
- Language-guided tracking using VLMs on custom data
- OCR-based automated classification systems
I take on projects where I can deliver real value.
Message me first — I'll give you an honest answer on
whether your problem fits my skillset before you commit
to anything.
Steps for completing your project
After purchasing the project, send requirements so Usman can start the project.
Delivery time starts when Usman receives requirements from you.
Usman works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Data Strategy Assessment
We start with a technical consultation. I analyze your specific business data, pinpoint where standard models are failing, and outline a custom data strategy to prepare your dataset for the model.
Custom Architecture Design
I design the custom layout for your vision-language model. This involves setting up the layer strategy, deciding which core foundational weights to protect, and identifying which layers to modify