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senior-computer-vision

Builds and optimizes computer vision systems for detection, segmentation, and production inference.

2|1|Updated Jan 29, 2026
Search Tags:#mlops#model training#pytorch#computer vision#object detection#opencv#inference optimization
One-click install
npx skills add https://github.com/bhaktofmahakal/ai-counsellor-hf --skill senior-computer-vision-bhaktofmahakal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: senior-computer-vision
Source: https://github.com/bhaktofmahakal/ai-counsellor-hf/tree/main/.claude/skills/senior-computer-vision
Command: npx skills add https://github.com/bhaktofmahakal/ai-counsellor-hf --skill senior-computer-vision-bhaktofmahakal

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve? Designing, training, and deploying computer vision systems involves complex decisions across model architectures, dataset pipelines, and inference optimization that are difficult to get right without senior-level guidance. ## Core Features & Use Cases - Vision Model Training: Train custom vision models using PyTorch-based workflows via the vision_model_trainer.py script. - Inference Optimization: Analyze and optimize inference pipelines for latency and throughput targets using the inference_optimizer.py script. - Dataset Pipeline Construction: Build scalable dataset processing pipelines with the dataset_pipeline_builder.py script. - Use Case: A team building a real-time object detection system can use this Skill to design the architecture, train a YOLO-based model, and optimize the inference pipeline to meet P95 latency under 100ms. ## Quick Start Ask the AI to design and train an object detection model for your dataset and optimize its inference pipeline for production deployment.

Frequently Asked Questions about senior-computer-vision

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I train a custom object detection model with PyTorch?▼

Use the vision_model_trainer.py script with your input dataset and output directory to run a training workflow. The skill's references cover architecture selection and performance tuning for detection models like YOLO.

How to optimize inference latency for vision models in production?▼

Run the inference_optimizer.py script against your project to analyze the pipeline. The skill recommends batching, caching, load balancing, and auto-scaling strategies targeting P95 latency under 100ms.

What tools does this skill use for computer vision tasks?▼

The skill centers on PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers, with deployment via Docker and Kubernetes and monitoring through MLflow and Prometheus.

Can I use this skill for real-time video analysis?▼

Yes, the skill covers video analysis and real-time processing patterns, including high-throughput inference systems with batching and auto-scaling for concurrent workloads.

What are the limitations of the provided training scripts?▼

The scripts provide production-grade scaffolding with validation and logging, but the core execution logic is a template that must be implemented with your actual training and optimization code.

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