I will develop ai based object detection, segmentation and tracking solutions

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Noman Shabbir

About this gig

CORE DETECTION EXPERTISE:

  • Foundation Models: Zero-shot detection, VLMs(Florence/paligemma 2) LoRA fine-tuning, CLIP/DINO optimization
  • CNN Architectures: YOLO/RetinaNet (single-stage), R-CNN/Mask R-CNN (two-stage), FPN architectures
  • Transformers: Swin/Swift Transformer, DETR, DINOv2 backbone integration
  • Few-shot Learning: Transfer learning, knowledge distillation, active learning pipelines

ADVANCED TRACKING:

  • SOTA MOT: ByteTrack, DeepSORT (custom embeddings), StrongSORT, Norfair
  • Capabilities: Multi-camera re-ID, occlusion handling, trajectory forecasting

TECHNICAL STACK:

  • Frameworks: MMDetection, Detectron2, TF-OD API, custom architecture
  • Libraries: PyTorch, TensorFlow/Keras, OpenCV, Supervision

DOMAIN EXPERTISE:

  • Industrial: Anomaly detection, PPE compliance, quality control
  • Surveillance: Real-time threat monitoring, perimeter protection, behavior analysis
  • Transportation: Vehicle tracking, ALPR, traffic analysis
  • Retail: product recognition, automated inventory


APIs:

Microsoft Computer Vision AI, Google Cloud Vision API

Expertise:

Image processing, Feature learning, Classification

Programming language:

Python, C++, SQL

Tools:

Pytorch, TensorFlow, Opencv, MLflow

Get to know Noman Shabbir

Noman Shabbir

Machine Learning, Computer Vision, Home Automation and Robotics Expert

  • FromPakistan
  • Member sinceJul 2023
  • Languages

    Urdu, Punjabi, English, German
Hello! I’m a Mechatronics Engineer with extensive expertise in Python and a strong focus on computer vision and machine learning. I excel in designing and optimizing advanced models for diverse applications, including segmentation and object detection, using state-of-the-art technologies such as CNN,Yolo, Transformers, and more. Machine Learning Frameworks: Proficient in TensorFlow and PyTorch for developing and refining models for tasks like classification, regression, and segmentation.

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