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Download the output and check it yourself.

Every claim on this site is about what comes out of a NameFrame run, so here is what comes out of one. These packs are cut from a real Map_Airbase_Demo capture on Unreal Engine 5.8: unmodified frames, the instance buffers the labels were derived from, and the reports that graded the run, including the check it failed.

Source run
40 frames
Instances
4,378
Resolution
1920×1080
Classes
person, car, tank, container, crate, barrel
Seed
67

Airbase detection sample

49 MB

A working detection and segmentation set: twelve frames spread across the run, with YOLO and COCO labels, the instance ID buffers they were derived from, and the metadata that says where every camera and object was.

Frames
12
Annotated instances
1,813
Format
ZIP
  • images/ rendered RGB frames, unmodified PNG
  • segmentation/ per-instance ID buffers, one colour per instance
  • labels/ YOLO boxes, one text file per frame
  • annotations/instances.json 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
Download 49 MB

SHA-256ac54025db5789bc58e62a54474b3c0ced5563a64c984baf01e155a7e71f17f94

Airbase every-modality sample

14 MB

Fewer frames, everything the engine wrote. Includes the raw float32 depth arrays in metres, which are too large to ship for a whole run but are the thing worth checking if you care about geometry.

Frames
3
Annotated instances
562
Format
ZIP
  • images/ rendered RGB frames, unmodified PNG
  • segmentation/ per-instance ID buffers, one colour per instance
  • labels/ YOLO boxes, one text file per frame
  • annotations/instances.json 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
  • depth/ float32 metres per pixel, as NumPy .npy arrays
Download 14 MB

SHA-256c52922f1ee9db49abd42e00b3ddc25f19dfea83bd4ac0ad01362267d90769f22

What the frames look like

Twelve frames from across the run. The spawner placed the people, crates, barrels and containers; the vehicles and the airbase itself were already in the level. Every object either pack labels was placed or authored, never guessed.

Frame plugin_000000 with 129 labelled instancesFrame plugin_000003 with 122 labelled instancesFrame plugin_000006 with 120 labelled instancesFrame plugin_000009 with 103 labelled instancesFrame plugin_000012 with 135 labelled instancesFrame plugin_000015 with 119 labelled instancesFrame plugin_000018 with 104 labelled instancesFrame plugin_000021 with 109 labelled instancesFrame plugin_000024 with 129 labelled instancesFrame plugin_000027 with 128 labelled instancesFrame plugin_000030 with 106 labelled instancesFrame plugin_000033 with 102 labelled instances
Map_Airbase_Demo1920×1080 per frameReport previews, downscaled for the web

Terms, plainly

Free to download, read, train on and benchmark against. Publish whatever results you get, including bad ones. What you may not do is repackage the pack and sell it as your own dataset.

The images depict environment and prop content licensed for use inside Unreal Engine projects. Redistribution rights for that underlying content are not granted with these packs, so check the source licence before publishing derivatives. The full text ships as TERMS.txt inside each archive.

What this sample is not

It is not a benchmark. Forty frames from four camera zones on one map is a sample you can inspect, not a set anyone should report a number against. There is no held-out test split for the same reason.

If you need something specific, that is what a run is for. Tell us the classes, the conditions and the volume, and we will generate it.

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