--- pretty_name: "Airbase: Unreal Engine synthetic capture" license: other license_name: sample-capture-terms license_link: LICENSE task_categories: - object-detection - image-segmentation tags: - synthetic - synthetic-data - unreal-engine - computer-vision - object-detection - instance-segmentation - sim2real size_categories: - n<1K annotations_creators: - machine-generated source_datasets: - original configs: - config_name: default data_files: - split: train path: images/* --- # Airbase: a synthetic detection capture rendered in Unreal Engine 300 rendered frames from a single Unreal Engine environment, carrying 32,546 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 | `Map_Airbase_Demo` | | Frames | 300 | | Instances | 32,546 | | Resolution | 1280 x 960 | | Horizontal FOV | 64 deg | | Camera policy | `camera_zones_look_at_target`, 4 authored zones | | Lens profile | `realistic_drone at 0.6` | | Time of day | 07:00 to 19:00 | | Weather | scene (300 frames) | | Master seed | 67 | | Validator | grade A, 92.1 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 300 frames. ## Classes | id | class | instances | | --- | --- | --- | | 0 | person | 7,921 | | 1 | car | 1,811 | | 2 | tank | 570 | | 3 | container | 2,573 | | 4 | crate | 8,717 | | 5 | barrel | 10,954 | By COCO's scale thresholds: 22,648 small, 8,637 medium, 1,261 large. The mean box covers 0.13% 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 300 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 300 - Frames with no labels: 0 - Near-duplicate images: 2 - Cross-split leakage: 0 - Mean luminance 100.8; 18 underexposed frames, 0 overexposed - Reconciled frame by frame: 300 requested, 300 images, 300 label files, 32,546 annotations ## Known limitations - The validator took 7.58 points off for `class-imbalance`: rarest/commonest class ratio 0.052. - The validator took 0.33 points off for `near-duplicate-images`: visually near-identical images in one split (dHash <= 4). - Check `build-provenance` did not pass: BUILD_PROVENANCE_UNVERIFIED: UNVERIFIED_REPOSITORY_COMMIT. - Depth-probed people the engine could see, against people labelled: 3/7370 depth-probed people unlabelled (0.04%, 300/300 frames). - Camera placements are drawn from 4 authored zones under the `camera_zones_look_at_target` policy, so viewing angles and distances cover a narrow band. This is a sample of one environment, not a benchmark. - 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 .