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lila-bc-community
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missouri-camera-traps

Tasks:
Object Detection
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< 1K
Tags:
wildlife
camera-trap
conservation
object-detection
bounding-boxes
animals
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dict
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End of preview. Expand in Data Studio

Missouri Camera Traps — Detection

Camera-trap images with bounding-box annotations, from the LILA BC Missouri Camera Traps dataset.

Unofficial community mirror — not affiliated with or endorsed by LILA BC. This is an HF-native, viewer-ready object-detection build (parquet, dataset viewer renders boxes, trains in one command). For the full multi-dataset collection use the streaming loader society-ethics/lila_camera_traps.

What this is

This build keeps only the bounding-box–annotated subset of Missouri Camera Traps (--boxed-only): 947 images, ~956 boxes, 20 species. The original LILA dataset is larger (~25k images) and mostly sequence-level classification labels — only a subset carries boxes, so the class distribution here differs from the full set's headline species. Non-object labels (empty, human, etc.) are excluded.

Species (20 categories)

Species Boxes Species Boxes
agouti 87 great_tinamou 44
collared_peccary 82 common_opossum 44
red_deer 68 red_squirrel 39
red_brocket_deer 63 white-nosed_coati 38
ocelot 63 bird_spec 38
paca 57 spiny_rat 34
wild_boar 56 wood_mouse 29
white_tailed_deer 47 european_hare 28
roe_deer 46 red_fox 25
mouflon 45 coiban_agouti 23

Structure 

image     : Image
image_id  : string
width     : int32
height    : int32
objects   : { bbox: [x, y, w, h] (COCO xywh), category: ClassLabel, area: float32 }

Usage 

from datasets import load_dataset

ds = load_dataset("lila-bc-community/missouri-camera-traps", split="train")

Train an object detector (one command) 

hf jobs uv run --flavor l4x1 \
  https://huggingface.co/datasets/hugging-face-vision-trainer/scripts/raw/main/object_detection_training.py \
  --dataset_name lila-bc-community/missouri-camera-traps \
  --model_name_or_path facebook/detr-resnet-50 \
  --output_dir missouri-detector --do_train --do_eval --push_to_hub

Source, license & citation 

@article{zhang2016animal,
  title={Animal detection from highly cluttered natural scenes using spatiotemporal
         object region proposals and patch verification},
  author={Zhang, Zhi and He, Zhihai and Cao, Guitao and Cao, Wenming},
  journal={IEEE Transactions on Multimedia},
  volume={18}, number={10}, pages={2079--2092}, year={2016}, publisher={IEEE}
}

See also

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