Datasets:
text stringclasses 10
values |
|---|
1 0.3500 0.6200 0.3000 0.3400 |
1 0.5500 0.6000 0.2800 0.3200 |
1 0.2200 0.6600 0.2000 0.2600 |
2 0.4800 0.7000 0.3600 0.2800 |
2 0.3000 0.6800 0.3000 0.2500 |
2 0.7000 0.7200 0.2200 0.2000 |
3 0.6200 0.6600 0.2200 0.2000 |
0 0.4000 0.2500 0.1000 0.0900 |
3 0.7000 0.7000 0.2000 0.1800 |
0 0.5500 0.3000 0.1200 0.1000 |
Example Wildlife Detection Dataset with Additional Information
Version: 1.0
A small, synthetic camera-trap object-detection dataset (4 animal categories plus empty images) with bounding boxes in YOLO format, laid out like a real one: authors with affiliations, a publisher and rights holder, extra funding text, and an additional_info/ folder with the lists and statistics that came with it. Not real field data; the images are generated placeholders.
Dataset format
This dataset is distributed in YOLO training format:
image files split into train/val/test sets, described by a data.yaml config.
It has 5 classes: Ave, Cervus elaphus, Sus scrofa, Vulpes vulpes, Empty.
.
├── data.yaml ← YOLO dataset config (splits, classes)
├── images/
│ ├── train/ ← training images
│ ├── val/ ← validation images
│ └── test/ ← test images (if present)
├── labels/ ← training labels (if present in the source dataset)
│ ├── train/
│ ├── val/
│ └── test/
├── checksums-sha256.txt
├── CITATION.cff
└── LICENSE
Dataset statistics
| Split | Images | Labeled images | Objects |
|---|---|---|---|
| train | 8 | 8 | 10 |
| val | 3 | 3 | 3 |
| test | 3 | 3 | 3 |
| Class | Objects |
|---|---|
| Ave | 4 |
| Cervus elaphus | 5 |
| Sus scrofa | 4 |
| Vulpes vulpes | 3 |
| Empty | 0 |
Integrity verification
checksums-sha256.txt lists the SHA-256 hash of every file in this export, to verify
nothing was corrupted or truncated after downloading.
Source
Generated via wildintel-publisher.
Contributing
Questions, corrections and contributions to this dataset are welcome at https://github.com/wildintelproject/wildintel-publisher
Funding
This work is part of the WildINTEL project, funded by the Biodiversa+ Joint Research Call 2022-2023 "Improved transnational monitoring of biodiversity and ecosystem change for science and society (BiodivMon)". Biodiversa+ is the European co-funded biodiversity partnership supporting excellent research on biodiversity with an impact for policy and society. Biodiversa+ is part of the European Biodiversity Strategy for 2030 that aims to put Europe's biodiversity on a path to recovery by 2030 and is co-funded by the European Commission.
WildINTEL has been co-funded by the European Commission (GA No. 101052342) and the following funding organisations: Agencia Estatal de Investigación (Spain, PCI2023-145963-2, PCI2024-153489), National Science Centre (Poland, UMO-2023/05/Y/NZ8/00104), the Research Council of Norway (Norway, NFR350962) and the German Research Foundation (Germany).
Additional support was provided by the Example Foundation (project EX-2021-001).
License
This dataset is released under: CC-BY-NC-4.0. See the LICENSE file for the full text.
Citation
To cite this dataset:
Jane Researcher & John Roe (2026). Example Wildlife Detection Dataset with Additional Information (Version 1.0) [Data set]. University of Huelva. https://huggingface.co/datasets/wildintelproject/pruebatoydonana © 2026 University of Huelva
This citation was generated from the CITATION.cff file. See
About CITATION files
for more info.
Limitations and ethical considerations
Before using this dataset, users should review possible limitations related to class imbalance, annotation quality, environmental bias, and any privacy or sensitive information the images may contain.
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