You will get an edge-optimized Computer Vision model for wearable devices.
Project details
Edge First Computer Vision: Optimized Inference for Wearable Devices 🚀
Deploying vision models on wearables requires a strict balance between thermal management and power consumption. This service moves complex AI to the edge for devices with limited RAM.
Core Technical Features:
Hardware Specific Architecture: Model selection (MobileNet, Tiny YOLO, or Custom CNNs) is dictated by the target device’s TOPS and SRAM. 🧠
Precision Engineering: Implementation of Post Training Quantization and Weight Pruning to minimize model footprint while maintaining accuracy.
Environmental Adaptability: Training includes augmentation for motion blur and low lux levels common in wearable modules. 📸
Inference Optimization: Optimized binaries for TensorRT, TFLite, or OpenVINO on ARM or RISC V architectures.
Augmented Reality: Real time spatial awareness for AR glasses. 👓
Industrial Safety: On device PPE detection and hazard alerts for smart helmets. ⛑️
This service eliminates cloud dependency, providing a secure and power efficient vision solution that stays entirely on the wearable device.
Deploying vision models on wearables requires a strict balance between thermal management and power consumption. This service moves complex AI to the edge for devices with limited RAM.
Core Technical Features:
Hardware Specific Architecture: Model selection (MobileNet, Tiny YOLO, or Custom CNNs) is dictated by the target device’s TOPS and SRAM. 🧠
Precision Engineering: Implementation of Post Training Quantization and Weight Pruning to minimize model footprint while maintaining accuracy.
Environmental Adaptability: Training includes augmentation for motion blur and low lux levels common in wearable modules. 📸
Inference Optimization: Optimized binaries for TensorRT, TFLite, or OpenVINO on ARM or RISC V architectures.
Augmented Reality: Real time spatial awareness for AR glasses. 👓
Industrial Safety: On device PPE detection and hazard alerts for smart helmets. ⛑️
This service eliminates cloud dependency, providing a secure and power efficient vision solution that stays entirely on the wearable device.
Machine Learning Tools
BigDL, Keras, Minitab, OpenCV, Python, PyTorch, scikit-learn, SciPy, TensorFlowWhat's included
| Service Tiers |
Starter
$195
|
Standard
$395
|
Advanced
$895
|
|---|---|---|---|
| Delivery Time | 4 days | 8 days | 16 days |
Number of Revisions | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$30 - $100
Additional Revision
+$20
Additional Graph/Chart
(+ 1 Day)
+$20About Mohid
Computer Vision Expert | Edge AI, OCR, YOLO | AI Integration
Islamabad, Pakistan - 10:02 am local time
✅ Upgrade generic API wrappers into custom, high precision AI assets
✅ Scale automated data collection with bespoke object detection pipelines
✅ Achieve zero latency with on device machine learning architecture
You are probably here because... off the shelf AI models are not solving your specific business logic.
Sound familiar?
"Our standard OCR fails to read custom screens and noisy data"
"Cloud latency is ruining our real time video analytics"
"We need absolute millimeter precision for tracking, but pre built models drift"
"We have the visual data, but no pipeline to actually process it"
Most scaling tech projects hit this exact wall when moving from basic prototypes to production grade vision systems.
⚙️Recent Engineering Outcomes:
→ Automated Data Extraction: Built a complete pipeline utilizing custom object detection and OCR to extract real time data from complex slot machine interfaces.
→ Animation & Biometrics: Engineered an AI aligner featuring high precision viseme tracking and deep biometric mapping for exact lip sync animation.
→ Assistive Wearable Tech: Architected an intent classifier for visual assistance devices requiring rapid environmental processing and edge deployment.
→ Foundational Logic: Coded neural networks and logic gates from scratch using pure Python and NumPy to ensure absolute mathematical efficiency.
👁️How The Vision Engineering System Works
Building custom architecture from the ground up:
→Algorithmic Mapping: Formulating the core mathematical logic and knowledge based reasoning systems before writing a single line of code.
→ Model Development: Training custom neural networks for highly specific tasks like object tracking, facial landmark detection, and pattern matching.
→ Edge Deployment: Porting heavy models to SoCs (Microcontrollers, Jetson, Raspberry Pi) for zero latency inference without cloud dependency.
→ Pipeline Optimization: Structuring seamless image quality enhancement, noise reduction, and automated visual inspection protocols.
🧠What You Actually Get (Partnership Outcomes):
✅ Custom logic - AI systems tailored exactly to your unique hardware and data
✅ Reliable inference - Models that operate smoothly in constrained environments
✅ Scalable architecture - Cleanly written pipelines ready for future expansion
Deliverables We Build Together:
- Custom OCR & Real Time Object Detection Pipelines
- Facial Landmark & Precision Viseme Tracking Algorithms
- Edge AI porting and SoC integration
- Complete mathematical documentation and project architecture
Want an evaluation of your current visual data bottleneck? Send over your project requirements and hardware constraints. We can map out the exact custom architecture needed to solve it from the ground up.
Steps for completing your project
After purchasing the project, send requirements so Mohid can start the project.
Delivery time starts when Mohid receives requirements from you.
Mohid works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements Validation & Hardware Audit
The project kicks off with a review of the data and hardware specs provided in the requirements. Goal: Confirm the target device (e.g., ESP32, Jetson, or mobile) and its constraints (RAM, Flash, Power).
Dataset Preparation & Pre-processing
If the client provides data, I will clean, augment, and format it for the specific edge task. Task: Resizing images to match the model’s input layer and normalizing data to reduce computational load on the wearable.