CES — Bird-Eye-View Instance Segmentation
Jan 1, 2022
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1 min read
Project Details
01
Instance Segmentation
Trained SOLOv2 on customized datasets for automotive vision workloads.
02
Bird's-Eye View
Implemented OpenCV image blending to improve visual outputs of BEV images.
03
Multi-Model Design
Transformed instance segmentation into a multi-model architecture with multi-image classification.
04
ML Pipelines
Developed and executed end-to-end training and testing pipelines.
05
Model Explainability
Used LIME for YOLO feature extraction and visualization.
Tech Stack
Computer Vision
Python
NumPy
PyTorch
SOLOv2
mmdet
YOLO
LIME
Engineering Tools
OOP
PDB Debugger
OpenCV
VS Code
Linux
Authors
Lead Engineer — GenAI / Agentic AI & Applied ML
Lead Engineer with 5 years of hands-on experience developing and deploying GenAI, RAG, Agentic AI, and deep learning systems from scratch. I build production-grade AI platforms for realtime voice interaction, enterprise knowledge retrieval, regulatory search, and automated finance analysis. My work spans NLP, autonomous driving, computer vision, and Camera-LiDAR-Radar fusion, with a focus on reliable architecture, model optimization, and scalable data pipelines.
Stack: Python, GCP, GenAI, LLMs, Agents, Docker, FastAPI, Cloud SQL, Google ADK, Vertex AI, Agent Engine, Cloud Run, OpenAI Realtime API, WebSockets, Redis, MongoDB, Milvus, JWT, Docling, BM25, ModernBERT, vLLM, and FAISS.