We build computer vision systems that transform raw visual data into actionable intelligence. From real time

object detection and multi-class image segmentation to automated video analytics and document OCR, our

solutions leverage state of the art deep learning architectures including YOLOv8, Detectron2, Vision

Transformers (ViT), SAM (Segment Anything Model), and Florence 2 to deliver production grade visual AI.

• Object Detection and Tracking, Real time identification and tracking of objects in images and video streams

using YOLOv8, DETR, and custom CNN architectures. Applications in surveillance, retail analytics, autonomous

systems, and industrial monitoring

• Image Classification and Recognition, Multi-label classification systems powered by ResNet, EfficientNet,

ConvNeXt, and Vision Transformers. From medical imaging diagnostics to product quality inspection

• Semantic and Instance Segmentation, Pixel level understanding of visual scenes using Mask R-CNN, U-Net,

SAM (Segment Anything), and Florence 2. Critical for autonomous navigation, geospatial analysis, and medical

imaging

• Video Analytics and Action Recognition, Intelligent video processing for behavior detection, crowd analytics,

anomaly detection, and automated surveillance. Real time and batch processing pipelines

• Optical Character Recognition (OCR), Document digitization, invoice processing, license plate recognition, and

handwriting recognition using Tesseract, PaddleOCR, and custom transformer based OCR models

• Generative Visual AI, Image generation, style transfer, super resolution, and synthetic data generation using

Stable Diffusion, DALL-E, and diffusion based architectures for training data augmentation

Healthcare and Medical Imaging, Automated radiology analysis, pathology slide classification, retinal scan

assessment, and surgical assistance systems.

Manufacturing and Quality Control, Defect detection, assembly verification, and automated visual inspection

on production lines with sub-second latency.

Geospatial and Mining, Satellite imagery analysis, terrain classification, mineral prospectivity mapping, and

drillhole data visualization from aerial and drone footage.

Retail and E-Commerce, Visual search, product recognition, shelf monitoring, customer behavior analytics, and

automated inventory tracking.

Security and Surveillance, Facial recognition systems, perimeter monitoring, license plate recognition, and

anomaly detection in video feeds.

• Frameworks, PyTorch, TensorFlow, OpenCV, Detectron2, Ultralytics YOLOv8, Hugging Face Transformers,

Florence 2

• Architectures, ResNet, EfficientNet, ConvNeXt, Vision Transformers (ViT), DETR, Mask R-CNN, U-Net, SAM,

Stable Diffusion

• Deployment, NVIDIA TensorRT, ONNX Runtime, AWS Lambda, SageMaker, Docker, Triton Inference Server
• Edge AI, NVIDIA Jetson, TensorFlow Lite, OpenVINO for on device inference and real time processing
• Data Pipeline, Label Studio, CVAT for annotation, Albumentations for augmentation, DVC for version control,

Roboflow for dataset management

Every computer vision project at TensorLabs follows a structured path from concept to deployment:

Discovery and Feasibility, We assess your visual data, define success metrics, and determine the right model

architecture before writing a single line of code.

Data Pipeline and Annotation, We build robust data collection, cleaning, and annotation workflows. Our team

handles dataset curation, augmentation strategies, and quality assurance.

Model Development and Training, Iterative model training with experiment tracking via MLflow and Weights

and Biases, hyperparameter optimization, and rigorous evaluation against your production requirements.

Production Deployment, Containerized model serving with monitoring, A/B testing, and automated retraining

pipelines. Optimized for latency, throughput, and cost.

80+ projects shipped. If it doesn't run in production with real users, it doesn't count