---
title: "Computer Vision & Synthetic Data Guides"
description: "In-depth guides on synthetic data, dataset distillation, YOLO training, and CCTV video datasets for computer vision engineers."
canonical: https://simuletic.com/guides
html: https://simuletic.com/guides
site: Simuletic, https://simuletic.com/llms.txt
---

Guides

# Guides for computer-vision engineers.

These guides are written for engineers who build and ship detection models. They cover what synthetic data is and when it beats real footage, how video datasets for surveillance and activity recognition are structured, how to train YOLO on generated images, and newer ideas such as dataset distillation.

Each guide is practical rather than promotional: definitions first, then trade-offs, formats and evaluation. If you want to try the data itself, every dataset in the [catalogue](https://simuletic.com/datasets) has a free sample, and the [blog](https://simuletic.com/blog) explains how each one was built.

### What CCTV Training Data Costs in 2026: Free, Staged, Synthetic and Platforms

Public datasets, staged real footage from agencies, synthetic datasets and rendering platforms compared on price, labels, licence and what you actually get.

### Training a lying-on-the-ground detector on synthetic data only: D-FINE vs RF-DETR vs YOLO on real footage

D-FINE, RF-DETR and YOLO26 trained only on synthetic images, then scored on real CAUCAFall and GMDCSA-24 footage: method, full results, recipe and caveats.

### Violence and Aggression Detection Datasets Compared (2026)

RWF-2000, XD-Violence, UCF-Crime, UBI-Fights and four more fight datasets compared: size, source, labels, licence, and why CCTV hard negatives are missing.

### Weapon and Knife Detection Datasets Compared

Gun and knife detection datasets compared: Granada, Sohas, Mock Attack, YouTube-GDD and Kaggle sets. Size, classes, source, licence traps and look-alikes.

### Fall Detection Datasets: Public Options and What's Missing

UR Fall, Le2i, UP-Fall, Multicam, SisFall, FallAllD and thermal fall datasets compared: size, cameras, licence, and what is missing for CCTV and care homes.

### CCTV and Surveillance Datasets: Public Sources, Licences and Gaps (2026)

17 public CCTV and surveillance datasets compared by task, size, camera, labels and licence, from UCF-Crime to MOT20, and which ones allow commercial use.

### AI Security Camera Training Data: How Detectors Are Trained

Buyer's checklist for AI security camera training data: classes and hard negatives, YOLO/COCO labels, camera geometry, licences, validation and the EU AI Act.

### YOLO Licensing vs Dataset Licensing: What a Commercial Product Needs

Ultralytics YOLO is AGPL-3.0 unless you buy an Enterprise licence, and your training data has its own licence. What a commercial vision product needs from both.

### What Is Synthetic Data for Computer Vision? A Complete Guide

Synthetic data is reshaping how computer vision models are trained. Here's how it works, why it matters, and where it outperforms real-world data.

### What Is Dataset Distillation? How It Works and Why It Matters

Dataset distillation compresses massive training sets into tiny synthetic ones that train models nearly as well. Here's the full breakdown.

### 15 Video Datasets for Computer Vision, Compared (2026)

UCF101, Kinetics, NTU RGB+D, AVA, UCF-Crime, XD-Violence, UBnormal and more compared by size, classes, camera type and licence, plus what none of them covers.

### CCTV Video Anomaly Detection Datasets: A Practical Guide

How CCTV video anomaly detection datasets are built, which behaviors matter (falls, fights, weapon draws, loitering), and why synthetic CCTV wins.

### Human Activity Recognition Datasets: Benchmarks, Labels and Gaps

What HAR data looks like, how activity clips are labeled, why public benchmarks break in deployment, and how synthetic video covers rare activities.

### Synthetic Data for YOLO Training: A Step-by-Step Guide

Train YOLOv8 or YOLOv11 on synthetic data — dataset layout, data.yaml, training commands, domain-gap fixes, and synthetic vs manual labeling results.

### Synthetic Data vs Real Data for Computer Vision: A Practical Comparison

Head-to-head comparison of synthetic and real data for CV — cost, privacy, edge cases, annotation quality, and how to close the domain gap.

## Show us the alarm your model keeps getting wrong.

Send five frames from your cameras. We reply within one working day with the fastest way to fix it — a catalogue dataset, a stress test or a custom pack.

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Markdown version of https://simuletic.com/guides. Prices in USD, excl. VAT. Contact: contact@simuletic.com