DHRUV GARG
AI / ML ENGINEER
I ship PyTorch models to production — computer vision, generative systems, and the full stack around them.
SP-01 · Searchlight Protocol
Coarse-to-Fine
Detection
Small-object detection in high-resolution aerial imagery. A ResNet18 + LayerCAM guide fuses backbone layers 2/3/4 into a semantic heatmap; an NMS slicer extracts candidate crops; a YOLOv8 detector runs only on those — skipping 80%+ of empty background before fine inference.
NC-03 · Neural Canvas
Perceptual
Style Transfer
Feed-forward style transfer (Johnson 2016): 5 residual blocks with Instance Normalization, trained against a VGG-16 perceptual loss on MS-COCO across 4 styles. Shipped to a live Hugging Face Space with a callable /stylize API and ONNX export.
PQ-02 · PixelQueue
Async Annotation
Infrastructure
Human-in-the-loop vision pipeline: a React-Konva canvas behind an NGINX gateway → FastAPI → Celery workers, with PostgreSQL, a Redis broker, and MinIO blob storage. YOLO/SAM auto-labeling workers, COCO + YOLO export, an SSE event stream, and RBAC across admin / reviewer / annotator roles.
{"id": 1042, "class": "car"}
| 1042 | car | 0.94 |
| 1041 | truck | 0.89 |
$ pygog "Schedule a meeting with the ML team for Thursday"
Parsing intent…
Detected: Calendar.CreateEvent
Resolved: ML Team → 5 contacts
Found slot: Thu 2:00 PM – 3:00 PM
Event created · 5 attendees notified
$ ▍
PG-04 · PyGOG CLI
Agentic
Workspace CLI
Natural language in, tool routing out. A Go→Python reimplementation of gogcli that adds an LLM agent layer over 5 providers (DeepSeek, OpenAI, Gemini, Anthropic, OpenRouter), orchestrating across Gmail, Drive, Calendar, and Tasks — with web search, OS-keyring credential storage, and multi-account aliases.
Journey
Deep Learning
CNN architectures, training dynamics, evaluation pipelines from scratch.
Computer Vision
Monocular depth, custom detectors, real-time inference pipelines.
Generative AI
GANs, neural style transfer, VGG perceptual loss research.
Shipped to Prod
Neural Canvas on Hugging Face — research notebook to ONNX export.
Full-Stack ML
Async infra, LLM agent orchestration, production deployment.
Capabilities
Every capability below is bound to a system I shipped — not a self-rating.
-
01
Perception
Proven in Searchlight Protocol →Coarse-to-fine detection of small objects in high-resolution aerial imagery — slicing regions of interest and skipping 80%+ of empty background before fine inference.
- PyTorch
- YOLOv8
- LayerCAM
- OpenCV
- ONNX
-
02
Generation
Proven in Neural Canvas →Feed-forward neural style transfer trained against a VGG-16 perceptual loss, exported to ONNX and shipped as a live demo on Hugging Face.
- PyTorch
- VGG-16
- ONNX
- Hugging Face
- GANs
-
03
Infrastructure
Proven in PixelQueue →Async, human-in-the-loop annotation at scale: Celery workers behind a FastAPI gateway, a Redis broker, a PostgreSQL store, and a React-Konva labeling canvas.
- FastAPI
- Celery
- Redis
- PostgreSQL
- Docker
-
04
Agents
Proven in PyGOG CLI →Natural-language intent parsed into authenticated tool calls over 5 interchangeable LLM providers, orchestrated across Google Workspace — Gmail, Drive, Calendar, and Tasks — from a single command line.
- Typer
- LLM Agents
- OAuth2
- Function Calling
Proof
Get in Touch
dhruvgarg.garg123@gmail.com ↗Open to AI/ML research, internships,
and full-stack roles where perception systems matter.