Seeing the Unseen β trained models
Detector weights and derived bases from a controlled study of occlusion compensation in aerial wildlife monitoring, along two axes: single view vs multi view, and sensor (pixel) space vs embedding space.
Code
https://github.com/Faline-eco/SeeingTheUnseen-Replication
The replication package: the pipeline that produced these checkpoints, the
container it ran in, every scored metric behind the paper's tables, and a
step-by-step in REPLICATION.md. Run directory names here are the run
identifiers used there, so a checkpoint joins directly against metrics/*.json
under the arms key.
Contents
| path | what it is | count |
|---|---|---|
heads/ |
CenterNet-style detection heads (0.36 M params) trained on frozen embedding grids | 342 |
yolo/ |
YOLO26x detectors fine-tuned on the rendered imagery | 72 |
bases/ |
INSID3 positional subspaces fitted from Gaussian-noise probes | 4 |
pca/ |
PCA bases reducing each feature store to 128 dims | 17 |
Backbones are not included. DINOv3 and V-JEPA 2.1 are used frozen and unmodified β obtain them from their original sources. Only self-trained artefacts are here.
Nearly everything here is cross-validated. A run directory whose name carries
_f<fold> belongs to the scene-level five-fold grid; 390 of the 414 run
directories do. Superseded single-split runs have been removed.
Twenty-four single-split directories are deliberately kept, because two claims in the paper rest on them and nothing cross-validated replaces them:
yolo/<arm>_s<seed>(12) β the ground-truth-choice comparison, which scores each detector against both the central and the merged annotation.heads/alfs_2k_*_2k_{64,128,256}_s<seed>(12) β the PCA-width ablation behind the choice of 128 dimensions.
The cross-validation grid in yolo/ is the complete {ortho, alfs} Γ
{rgb, thermal} Γ 5 folds Γ 3 seeds = 60 runs behind the image-space result,
each with its results.csv and the args.yaml it was trained with.
Fine-tuned OWL-D checkpoints are not published. The metrics are in the
replication repository (metrics/owlft_rgb_f*.json) and the procedure in
docs/OWL_BASELINE.md, but each checkpoint carries a full DINOv3 backbone at
3.3 GB and the ten of them were judged not worth 33 GB here.
Naming
Directory names are the run identifiers used throughout the study, so they join
directly against the logged metrics in the replication repository (metrics/*.json,
under the arms key):
heads/cell_thermal_embed_multi_embed_multi_f0_s1337/
cell_<modality>_<cell>_<label>_f<fold>_s<seed>
yolo/ortho_thermal_f0_s1337/
<aperture>_<modality>_f<fold>_s<seed>
A name without _f<fold> is one of the 24 retained single-split runs described
above, not a fold of the cross-validation.
_debias32 marks arms with the INSID3 positional subspace projected out.
Cell families: embed_* (DINOv3, encode-then-integrate), vjepa_*
(V-JEPA 2.1), alfsembed_* (integrate-then-encode), and sensor_*,
realalfs_*, realortho_* (pixel space).
Protocol
Scene-level 5-fold cross-validation over 72 flights, balanced on hidden-box count, 3 seeds per fold. No frame from a validation flight appears in training.
Each fold and seed has its own checkpoint. Pick the checkpoint by fold and never pool across folds, or the held-out guarantee is lost.
- Heads: AdamW 3e-4, cosine schedule, batch 16, up to 60 epochs (patience 12), focal + 5x L1 loss.
- YOLO: 1024 px, batch 4, up to 100 epochs (patience 10).
Licence
MIT for heads/, pca/ and bases/ β see
LICENSE
in the replication repository, which covers the code as well.
AGPL-3.0 for yolo/ only. Those weights are fine-tuned from Ultralytics
YOLO26x and inherit its terms. AGPL-3.0 applies if you use those checkpoints; it
does not reach anything else here.
The repository is declared other rather than mit because the two cannot be
expressed in one field, and labelling the whole thing MIT would misrepresent the
YOLO weights.
Imagery derives from the public BAMBI dataset under its own terms. DINOv3 and V-JEPA 2.1 are used frozen and unmodified and are not redistributed here; obtain them from their original sources under their own licences.