NameFrame/dubrovnik-unreal-synthetic

Dubrovnik old town: a synthetic detection capture rendered in Unreal Engine

290 rendered frames from a single Unreal Engine environment, carrying 9,927 labelled object instances. Every box and every mask here is read out of the engine's own per-instance ID buffer at render time. No model produced these labels and no one drew them by hand, so a label is wrong only where the scene description behind it is wrong.

The capture

Engine	5.8.1-56057345+++UE5+Release-5.8
Map	L_Showcase
Frames	290
Instances	9,927
Resolution	1280 x 720
Horizontal FOV	62 deg
Camera policy	random, 0 authored zones
Lens profile	none
Time of day	08:00 to 19:00
Weather	scene (290 frames)
Master seed	20260907
Validator	grade B, 86.5 out of 100

Placement, pose and camera pose are sampled deterministically from the master seed (deterministic_per_logical_frame_and_instance), so the same seed over the same scene reproduces the same 290 frames.

Classes

id	class	instances
0	person	9,253
1	bicycle	0
2	car	0
3	vegetation	0

By COCO's scale thresholds: 7,399 small, 1,854 medium, 0 large. The mean box covers 0.08% of the frame, which makes this a small-object capture.

Layout

images/          rendered RGB frames, unmodified PNG
segmentation/    per-instance ID buffers, one colour per instance
labels/          YOLO boxes, one text file per frame
annotations/     the same labels in COCO format
metadata/        per-frame camera, actor and spawn-manifest records
reports/         this capture's own report and validation output
capture.json     the run contract: classes, camera policy, seed
data.yaml        class names, ready for a YOLO trainer

Frame N is the same scene across images/, segmentation/, labels/ and metadata/. No split is imposed: all 290 frames sit in one pool, so you can split them however the experiment needs.

Decode segmentation/*.png with a library that hands you channels in the order the file declares. The instance colours are exact 8-bit values, and a silent channel swap turns a correct mask lookup into an empty one.

Measured quality

This archive ships the validator's own output rather than a summary of it. From reports/validation.json:

• Corrupt images: 0 of 290
• Frames with no labels: 0
• Near-duplicate images: 87
• Cross-split leakage: 0
• Mean luminance 77.5; 13 underexposed frames, 0 overexposed
• Reconciled frame by frame: 290 requested, 290 images, 290 label files, 9,927 annotations

Known limitations

• The validator took 7.99 points off for class-imbalance: rarest/commonest class ratio 0.002.
• The validator took 5.0 points off for near-duplicate-images: visually near-identical images in one split (dHash <= 4).
• The validator took 0.36 points off for underexposed-images: mean luminance < 35.0.
• The validator took 0.17 points off for blurry-images: variance-of-Laplacian < 100.0.
• Check build-provenance did not pass: cannot read _local\dubrovnik300\dump\build_provenance.json: [Errno 2] No such file or directory: '_local\dubrovnik300\dump\build_provenance.json'.
• Check configuration-snapshot-integrity did not pass: configuration snapshot is missing: _local\dubrovnik300\dump\configuration_snapshot.json.
• Check spawn-manifest-integrity did not pass: capture spawn-manifest frame count 300 != 290; spawn_manifests.jsonl missing; spawn manifest index has 0 rows != 290 frame manifests.
• Depth-probed people the engine could see, against people labelled: 8/9289 engine-visible people unlabelled (0.09%, 290/290 frames).
• Rendered imagery carries a synthetic-to-real domain gap. Treat it as pre-training or augmentation material rather than a substitute for a real-world evaluation set.

Provenance and terms

The frames were rendered in Unreal Engine and labelled from the engine's instance buffers by NameFrameCapture, an editor plugin. capture.json carries the full build record, including the plugin and job hashes and the checks the build could not verify.

Environment and prop assets in the scene are licensed from third parties for use inside Unreal Engine projects. The rendered images and their labels are published here; the source assets are not, and no .uasset or map file is included. Read LICENSE before redistributing the imagery itself.

The capture pipeline, the other environments in this series and the measurements behind them are documented at https://getnameframe.com.