camtrap-distance

Reference-Conditioned Distance Intervals on Unseen Camera Traps — model weights

Trained weights for the paper Reference-Conditioned Distance Intervals on Unseen Camera Traps (Computer Vision for Ecology Workshop, ECCV 2026). The model is a Depth Anything V2 metric-outdoor backbone with a 7-channel input (target photo, reference flag photo, reference distance map) and a monotone quantile head. For each pixel it predicts horizontal ground distance in metres as a median and a 90% interval, [q05, q50, q95].

• Code: https://github.com/toqitahamid/camtrap-distance
• Dataset: https://huggingface.co/datasets/toqi/camtrap-distance-flags

Files

seed1/best/   config.json, model.safetensors   training seed 1
seed2/best/   config.json, model.safetensors   training seed 2

seed1/best is the checkpoint to use. seed2/best is the second training run; the paper reports the mean of the two.

Results

Held-out test cameras of the flag survey (12 of 62 cameras, 801 markers), aligned-reference path.

Usage

The checkpoint has a 7-channel patch embedding and a wrapped head, so it is loaded through the code repository rather than AutoModel.

hf download toqi/camtrap-distance --local-dir outputs/paper-ckpt

from src.calibration.ground_plane import load_calibration
from src.network.model import load_checkpoint
from src.network.predict import predict

model = load_checkpoint("outputs/paper-ckpt/seed1/best")
calibration = load_calibration("MAS_CAM04", "IMG_0001")
p = predict(model, "new_photo.jpg", "data/flaglabel-dataset/MAS_CAM04/IMG_0001.JPG", calibration)
p.q05, p.q50, p.q95      # (H, W) arrays, metres

See the code repository for installation, the survey data, and training.

Licence

CC BY-NC 4.0. The weights derive from Depth-Anything-V2-Large, which is released under CC BY-NC 4.0. The code is MIT.

Citation

@inproceedings{sarker2026reference,
  title     = {Reference-Conditioned Distance Intervals on Unseen Camera Traps},
  author    = {Sarker, Toqi Tahamid and Islam, Taminul and Morelock, Seth J.
               and Bastille-Rousseau, Guillaume and Ahmed, Khaled R.},
  booktitle = {Computer Vision for Ecology Workshop, European Conference on Computer Vision (ECCV)},
  year      = {2026}
}