Datasets:

forgottenl5
/

swarmlights

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 84, in _split_generators
                  raise ValueError(
                  ...<2 lines>...
                  )
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

SwarmLights-1K v2.1 (synthetic)

Can a computer keep track of 1,000 identical lights?

Data (8 show archives + T5 baseline outputs, about 2.1 GB): https://huggingface.co/datasets/forgottenl5/swarmlights
Code, scoring and docs: https://github.com/forgottenl5/swarmlights
Checksums of every archive: checksums/ARCHIVES.sha256

SwarmLights is a synthetic benchmark for tracking every drone in a dense night-time drone light show from three calibrated audience-side cameras. Shows have 500 or 1,000 drones, with exact 3-D ground truth for every drone in every frame. In our (non-exhaustive) search, the densest public drone-tracking dataset we found had about 16 drones per frame.

Synthetic data. Generated by the SwarmLogic simulator. Not a real show, and not validated against real footage with ground truth. Rendering is idealised (point-like LEDs, exact calibration, synchronised cameras, no camera shake). See Limits.

The open problem

With exact detections, the T5 baseline is near-perfect on every show (airborne IDF1 ≥ 0.995). From the rendered camera images it falls to 0.26–0.53, because neighbouring lights merge into one blob, formations seen edge-on collapse into a few blobs, and the front of a 3-D shape hides the back.

On every show, both three-camera trackers still score below the best single camera. Making three cameras beat one in dense formations is the challenge this benchmark sets.

Two failure modes are documented (see docs/):

Ghost points: two cameras' lines of sight through two different drones cross where there is no drone. In regular formations this is common. Third-camera verification of new tracks (baseline T5) removes 48–82 % of false tracks.
Drones inside merged blobs: a real drone hidden in a merged blob in one camera cannot be confirmed by three cameras, so it is missed. This is open.

What is in it

8 shows, 8 different choreographies (500 and 1,000 drones, seeds 20–23). One archive per show; inside:

video_K1.mp4, video_K2.mp4, video_K3.mp4: camera videos, 1920×1080, 10 fps, H.264 with phone-like compression; coloured LEDs, glow, sensor noise and 10 non-drone lights near the horizon.
calibration.json: exact camera positions, orientations and focal length.
gt.csv.gz: every drone in every frame: 3-D position (m), LED colour, pixel position and in-frame flag per camera.
distractors.json: the non-drone lights (they must not be tracked).
detections_K{1,2,3}.csv.gz: reference detections (frame,u,v, pixels) from a simple blob detector, used by the baselines.
show.json: choreography and phases; spacing.json: safety checks.
baselines/, metrics.json: outputs and scores of the single-camera and T4c baselines.
SHA256SUMS: a checksum for every file.

baselines_T5.tar adds the T5 baseline outputs and scores for all 8 shows. It is a separate archive so the show archives stay byte-identical to their registered fingerprints.

Flight paths are physically valid: speed ≤ 4 m/s, acceleration ≤ 2 m/s², and every pair of drones at least 2.12 m apart in every frame (checked).

Benchmark and scoring

Track every airborne drone in 3-D, then run:

pip install numpy pandas scipy motmetrics
python score.py your_tracks.csv <show_dir>

Reproducing the baselines

python baselines/run_baseline.py T5 <show_dir> t5.csv.gz (or T4c) reruns a baseline from the reference detections.

T5 reproduces the published outputs byte for byte.
T4c does not quite: the published T4c runs used unrounded detections, while the released detection files are rounded to 0.001 px. The tracker is sensitive enough that this shifts its scores slightly (for example n500 s22 airborne IDF1 0.454 rerun vs 0.448 published).
Rerun on all 8 shows from the released detections, T4c airborne IDF1 moves by at most 0.006 and MOTA by at most 0.006, except n1000 s22 (MOTA 0.274 published, 0.318 rerun). On these identical inputs T5 still has higher IDF1 than T4c on 6 of 8 shows, the same outcome as the registered comparison.

your_tracks.csv has columns frame,track,x,y,z (0-based frame, your identity number, metres in the gt.csv.gz frame). The script prints MOTA, IDF1, misses, false positives and ID switches. Rules: ground truth is every drone seen by at least 2 of the 3 cameras; match distance 1.0 m; airborne frames only (every phase except the take-off climb and final descent). score.py reproduces the published baseline scores (checked on T5 and T4c; the CSV outputs are rounded to 0.1 mm, which can move a single match at the 1 m boundary).

Baselines (airborne, mean of 4 shows)

Baseline	Input	500 drones MOTA / IDF1	1,000 drones MOTA / IDF1
Best single camera (2-D, pixels)	one video	0.715 / 0.587	0.620 / 0.515
T4c: three-camera 3-D	three videos	0.415 / 0.451	0.206 / 0.365
T5: T4c + shared detections + third-view check	three videos	0.512 / 0.486	0.377 / 0.401
T4c with exact detections (ceiling)	ground truth + 0.3 px noise	1.000 / 0.999	0.925 / 0.835
T5 with exact detections (ceiling)	ground truth + 0.3 px noise	1.000 / 0.999	0.996 / 0.996

Single-camera and 3-D scores are not strictly comparable (pixels vs metres; the 2-D score only counts drones visible to that camera), but the gap is large and consistent. Per-show numbers are in docs/DATASET_V2_1_RESULTS.md, docs/DATASET_T5_RESULTS.md and docs/DATASET_T5_RESULTS_ADDENDUM_1.md. The docs are published as written; references in them to other documents or to core/ modules point to the private project and are not included.

How it was made and checked

Every study was written down and fingerprinted (SHA-256) before it ran, and code was frozen before each build. These fingerprints were recorded privately, not publicly timestamped, so the timing has to be taken on trust; future studies will post fingerprints publicly before running. The generator is not public, so these checks cannot be rerun from seeds by others; score.py, the baselines and the per-file checksums can. Checks: frames rebuild from seeds (minimum PSNR 39 dB), ground-truth pixel positions recompute to < 0.01 px, detections reproduce exactly from the videos, and all files are checksummed.

Checking the fingerprints yourself. sha256sum -c checksums/REGISTERED_FILES.sha256 checks every released file that has a registered fingerprint: the three tracker files (the exact code frozen before scoring), the pre-registrations, the amendment and the results. The original fingerprint lists in docs/ (V2_1_FROZEN.sha256, T5_FROZEN.sha256, T5_RESULTS.sha256) are published unchanged, so they use the private project's paths (for example pilot_v2/mv_tracker5.py is baselines/mv_tracker5.py here) and also list files that are not released (the generator, build and analysis scripts). Running sha256sum -c on those lists directly will therefore report missing files.

Failed predictions are reported as failures, including:

v2.1 W3: T4c's ceiling fell below 0.95 MOTA on 2 of the 4 1,000-drone shows.
T5 P2: T5 did not beat the best single camera on any show (0 of 8; predicted ≥ 4).
T5 was amended once before scoring, after its first version failed the development gate. The amendment, the failed version and both fingerprints are in docs/.

Limits

Synthetic: idealised LEDs, one camera layout, exact calibration, no camera shake, weather or lens distortion.
Realism check v1 against two public phone videos (Wikimedia Commons, CC BY / CC BY-SA): dot size and crowding are within the real range; colour, clipping and compression were added in v2.1. This is a weak check with no ground truth.
The baselines were developed on separate development shows, not on these 8.
Not for safety certification of real shows.

Licence and credit

Data: CC BY-NC 4.0. Free for research and other non-commercial use, with credit. Commercial use, including training commercial products, needs a separate licence: open an issue on GitHub to ask.
Code: Apache-2.0 for score.py and the two baseline trackers.
Not released: the SwarmLogic planning engine and show generator. The data was produced with them; the build checks below were run by the author.
Cite as: SwarmLights-1K v2.1 (2026), forgottenl5 / SwarmLogic. https://github.com/forgottenl5/swarmlights
Questions and results: open an issue on GitHub.
Need data for other formations, camera layouts or swarm sizes? Open an issue to ask about custom synthetic data.

Intended use

Research on dense multi-object tracking, multi-view association and crowded-scene detection, for drone light shows and similar civilian settings.

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