lila-bc-community/channel-islands-camera-traps
Channel Islands Camera Traps — Detection Camera-trap images with bounding-box annotations from the Channel Islands, California, provided by The Nature Conservancy and distributed via LILA BC. 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… See the full description on the dataset page: https://huggingface.co/datasets/lila-bc-community/channel-islands-camera-traps.
Channel Islands Camera Traps — Detection
Camera-trap images with bounding-box annotations from the Channel Islands, California, provided by The Nature Conservancy and distributed via LILA BC.
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
An example-sized subset: 12,000 bounding-box–annotated images sampled from the full dataset (~246k images, ~126k of which carry real animal boxes) using --boxed-only --max-images 12000. Empty frames (which LILA tags with an empty box) and human labels (LILA removes the images for privacy) are excluded — see Annotation cleaning.
- Images: 11,996
- Boxes: 14,843
- Classes: 5
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/channel-islands-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/channel-islands-camera-traps \
--model_name_or_path facebook/detr-resnet-50 \
--output_dir channel-islands-detector --do_train --do_eval --push_to_hubAnnotation cleaning
Only genuine object boxes are kept: annotations with zero/negative-size boxes or non-object / privacy labels (empty, human, person, unidentified, …) are dropped. This is why the class set is the 5 animal classes rather than including empty/human.
Source, license & citation
- Source: LILA BC — Channel Islands Camera Traps
- Data provider: The Nature Conservancy
- License: Community Data License Agreement — Permissive 1.0
- Built with: `uv-scripts/lila-detection`
build_lila_detection.py
The Nature Conservancy (2021): Channel Islands Camera Traps 1.0. The Nature Conservancy. Dataset.
For questions about the source dataset, contact Nathaniel Rindlaub (nathaniel.rindlaub@TNC.ORG).
See also
- `society-ethics/lila_camera_traps` — comprehensive streaming loader (all LILA sets)
- `imageomics/IDLE-OO-Camera-Traps` — balanced classification benchmark
- `lila-bc-community/ena24-detection` · `lila-bc-community/missouri-camera-traps` — sibling detection sets
