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Computer Vision Services

Harness the power of advanced Computer Vision Services to elevate your AI capabilities. Our custom solutions, from model training to predictive analytics and deep learning models, are designed to optimize performance and deliver innovative results across diverse industries.
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What We Deliver

End-to-end CV solutions from prototyping to edge deployment:

Custom Model Development

Tailored for your use case.

Optimized Inference

Deploy fast, low-latency models on any device.

MLOps Integration

Monitor, retrain, and scale seamlessly.

Key Niches & Visual Demos Object Detection Semantic Segmentation Image Classification OCR & Document Analysis Facial Recognition

facial-recognition
Image Classification
Object Detection
OCR & Document Analysis
SemanticInstance Segmentation

Object Detection

Frameworks

YOLOv8, Faster R-CNN, EfficientDet.

Use Cases

Surveillance, retail inventory, drones.

Semantic Segmentation

Frameworks

Mask R-CNN, U-Net, Detectron2.

Use Cases

Medical imaging, autonomous robots.

Image Classification

Frameworks

ResNet, MobileNet, Vision Transformers (ViT).

Use Cases

Quality control, content moderation.

OCR & Document Analysis

Frameworks

Tesseract, EasyOCR, AWS Textract.

Use Cases

Invoice processing, license plate recognition.

Facial Recognition

Frameworks

FaceNet, DeepFace, OpenCV.

Use Cases

Security systems, personalized retail.

Popular Frameworks & Deployment

Core Frameworks

TensorFlow/Keras | PyTorch | OpenCV (classic CV).
YOLO Series: Balance speed + accuracy for real-time apps.

Transformers

ViT, DETR for cutting-edge performance.
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Mobile Deployment

TensorFlow Lite (TFLite): Optimized models for Android/iOS.
Core ML: Apple device integration.

Mobile App

App using TFLite to detect objects on a phone camera.
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Edge/Cloud Deployment

TensorRT: NVIDIA GPU-accelerated inference.
ONNX Runtime: Cross-platform compatibility.

REST APIs

Flask/Django for cloud-based CV services.
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Accuracy Boosting Techniques

Data Augmentation

Rotate, flip, or adjust lighting to diversify training data.

Transfer
Learning

Start with pre-trained models (ImageNet weights).

Hyperparameter Tuning

Optimize learning rates, batch sizes.

Post
Processing

NMS (Non-Max Suppression) to reduce duplicate detections.

Hardware Acceleration

Quantization (FP32 → INT8) for faster edge inference.

MLOps for Computer Vision

Train

Use tools like Roboflow or Label Studio for annotation → training

Convert

Optimize models to TFLite/TensorRT/
ONNX.

Deploy

AWS SageMaker, Azure ML. NVIDIA Jetson, Raspberry Pi.

Monitor

Track model drift with tools like Weights & Biases or Prometheus.

Retrain

Automatically update models with
new data.

Why Choose Us

Speed vs Accuracy

Balance based on your needs (YOLO for speed, ViT for accuracy).

Privacy-First

On-premise deployment for sensitive data.

End-to-End Ownership

From labeling to post-deployment support.

FAQ (Collapsible Section)

A: Yes! We optimize with TFLite/TensorRT for Raspberry Pi, Jetson Nano, etc.

A: Frame-by-frame processing + temporal modeling (e.g., OpenCV + LSTM).