Engineering CVMLDetection

Senior Computer Vision

Object detection, image segmentation, CNN/ViT architectures, and deployment.

$skillfish add ~skills/senior-computer-vision

Real Examples Get Started Models & Context

Real Conversations

See how the Senior Computer Vision skill responds to actual requests. These are representative examples of what your AI agent will produce.

You

Building a retail shelf audit system — need to detect ~80 product SKUs in real time on a store tablet (Snapdragon 8cx). Should I use YOLOv8 or DETR?

Agent

Recommendation: YOLOv8n or YOLOv8s — not DETR

DETR is a transformer-based detector optimized for accuracy, not latency. On a Snapdragon 8cx without a dedicated NPU, DETR inference will run 500ms-2s per frame — unusable for real-time.

YOLOv8 comparison for your constraint:

Model	mAP50 (COCO)	Latency (CPU)	Size
YOLOv8n	37.3	~45ms	6.3MB
YOLOv8s	44.9	~90ms	22MB
YOLOv8m	50.2	~200ms	52MB

For 80 SKUs on-device, start with YOLOv8s — good accuracy/speed balance.

Fine-tuning pipeline for your 80 SKUs:

from ultralytics import YOLO

model = YOLO('yolov8s.pt')  # start from COCO pretrained weights
results = model.train(
    data='retail_skus.yaml',
    epochs=100,
    imgsz=640,
    batch=16,
    augment=True,  # mosaic, mixup, HSV shifts — critical for retail lighting variance
)
# Export for on-device inference
model.export(format='onnx', opset=12, simplify=True)

Data requirements: ~200-500 images per SKU, captured under store lighting conditions. Use LabelStudio for annotation. Augment heavily for rotation and partial occlusion (products get partially blocked by other items).

On-device runtime: ONNX Runtime with XNNPACK backend gives ~2x speedup over vanilla CPU on Snapdragon.

You

My ResNet50 + FPN defect detection model gets mAP50 of 0.41 on validation but only 0.18 on the test set from the factory floor. Training on 2,000 labeled images.

Agent

Gap of 0.41 → 0.18 is a distribution shift problem, not a capacity problem.

Diagnose first:

import torchvision.transforms.functional as TF
from PIL import Image
import numpy as np

# Compare image statistics between train and test sets
train_means = [np.array(Image.open(p)).mean() for p in train_paths[:200]]
test_means = [np.array(Image.open(p)).mean() for p in test_paths[:200]]
print(f"Train brightness: {np.mean(train_means):.1f} | Test: {np.mean(test_means):.1f}")
# Large gap here = lighting condition mismatch

Common factory floor distribution shifts:

Lighting — training images from controlled setup, test from production line with varying ambient light
Camera angle/distance — slight mount variation changes defect apparent size
Product variation — new batch of material looks slightly different

Fix 1 — Aggressive augmentation during training:

transforms.Compose([
    transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.3, hue=0.1),
    transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.9, 1.1)),
    transforms.GaussianBlur(kernel_size=3, sigma=(0.1, 2.0)),
])

Fix 2 — Collect 100-200 labeled images from the actual test environment and add to training set. This alone usually closes 70% of the distribution gap.

Fix 3 — If you can't relabel: Try test-time augmentation (TTA) — average predictions across 4 augmented versions of each test image. Typically +3-5 mAP points for free.

Get Started

How to install and use this skill in your preferred environment.

Skills are designed for AI coding agents (Claude Code, Cursor, Windsurf) and IDE-based workflows where the agent can read files, run scripts, and act on your codebase. Web-based AI can use the knowledge and frameworks, but won't have tool access.

Models & Context

Which AI models and context windows work best with this skill.

Recommended Models

Best

Claude Opus 4 Claude Sonnet 4 GPT-4.1 Gemini 2.5 Pro Grok 3 Kimi K2

Good

Claude Haiku 4.5 GPT-4.1 mini Gemini 2.5 Flash Grok 3 mini

Larger models produce more detailed, production-ready outputs.

Context Window

This skill's SKILL.md is typically 3–10 KB — fits in any modern context window.

8K Skill only

32K+ Skill + conversation

100K+ Skill + references + codebase

All current frontier models (Claude, GPT, Gemini) support 100K+ context. Use the full window for complex multi-service work.

Pro tips for best results

1

Be specific

Include numbers — users, budget, RPS — so the skill can size the architecture.

2

Share constraints

Compliance needs, team size, and existing stack all improve the output.

3

Iterate

Start with a high-level design, then ask follow-ups for IaC, cost analysis, or security review.

4

Combine skills

Pair with companion skills below for end-to-end coverage.

Ready to try Senior Computer Vision?

Install the skill and start getting expert-level guidance in your workflow — any agent, any IDE.

$skillfish add ~skills/senior-computer-vision

← Browse all 169 skills