README.md

---
license: pddl
language:
- en
pretty_name: LILA BC Camera Trap Data
tags:
- biology
- image
- animals
- CV
- camera traps
size_categories:
- 1M<n<10M
---

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# Dataset Card for LILA BC Camera Trap Data

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## Dataset Description

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- **Homepage:** 
- **Repository:** [related project repo]
- **Paper:** 
- **Leaderboard:** 
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### Dataset Summary

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This dataset contains the LILA BC full camera trap information with notebook ([`lilabc_CT.ipynb`](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/main/notebooks/lilabc_CT.ipynb)) exploring available data. The last run of this (in [commit 010ecf0](https://huggingface.co/datasets/imageomics/lila-bc-camera/commit/010ecf0c6a2e0c99c9481cea793d8b1556b5c71e)) uses and produces the lila CSVs found [here](https://huggingface.co/datasets/imageomics/lila-bc-camera/tree/010ecf0c6a2e0c99c9481cea793d8b1556b5c71e/data).
More details on this are below in [Data Instances](#data-instances).

**Repo file description at [commit 87e2e4d](https://huggingface.co/datasets/imageomics/lila-bc-camera/tree/87e2e4d46cf1e8daadd74b7738856a1e30754de3) when we were considering it for BioCLIP v1 testing:**

Images have been deduplicated and reduced down to species designation, with the main CSV filtered to just those with species labels and only one animal per image. This was done by pulling the first instance of an animal so that there are not repeat images of the same animal from essentially the same time.

The deduplicated collection ([lila_image_urls_and_labels_species.csv](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/f2d596714c46bf30edf1f45efe88b3a09b3c5f81/data/lila_image_urls_and_labels_species.csv)) has 6,365,985 images (compared to the full dataset of 16,833,848 at time of download). Its [associated taxonomy mapping release](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/f2d596714c46bf30edf1f45efe88b3a09b3c5f81/data/lila-taxonomy-mapping_release.csv).

See the [LILA BC HF Dataset](https://huggingface.co/datasets/society-ethics/lila_camera_traps) for more inforamtion and updated data.



### Supported Tasks and Leaderboards
[More Information Needed]

### Languages
[More Information Needed]

## Dataset Structure

```
/dataset/
    data/
        lila-taxonomy-mapping_release.csv
        lila_image_urls_and_labels.csv
        lila_image_urls_and_labels_species.csv    # Outdated
        lila_image_urls_and_labels_wHumans.csv
    notebooks/
        lilabc_CT.ipynb
        lilabc_CT.py
```


### Data Instances

The [`data/lila_image_urls_and_labels.csv`](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/010ecf0c6a2e0c99c9481cea793d8b1556b5c71e/data/lila_image_urls_and_labels.csv) has all images with non-taxa (identified by `scientific_name`, `common_name`, and `kingdom` are null) or `human` original labels filtered out and has 10,104,328 images.
7,521,712 have full 7-rank taxa, with 891 unique 7-tuple strings (908 unique including subranks), with 890 unique scientific names -- this count is from before humans were removed (there are 257,159 images with humans listed and they do have full 7-rank taxa).
Final version at this stage has 9,849,119 images, 907 unique scientific names.

**annotation_level**
```
sequence    4156306
image       2892394
unknown     2886844
```

**non-taxa labels:**
```
original_label
problem                   288579
blurred                   184620
ignore                    177546
vehicle                    26445
unknown                    26170
snow on lens               17552
foggy lens                 15832
vegetation obstruction      6994
malfunction                 5640
unclassifiable              3484
motorcycle                  3423
misdirected                 2832
other                       2474
unidentifiable              1472
foggy weather               1380
lens obscured                866
sun                          835
end                          616
fire                         578
misfire                      400
eye_shine                    328
start                        321
tilted                        56
unidentified                  39
```

**Datasets with the non-taxa labels:**
```
dataset_name
SWG Camera Traps                    650745
Idaho Camera Traps                   66339
NACTI                                26015
WCS Camera Traps                     18320
Wellington Camera Traps               3484
Orinoquia Camera Traps                1280
Island Conservation Camera Traps      1269
Snapshot Serengeti                     568
ENA24                                  293
Channel Islands Camera Traps           159
Snapshot Mountain Zebra                  7
Snapshot Camdeboo                        3
```

### Data Fields
[More Information Needed]
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### Data Splits
[More Information Needed]
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## Dataset Creation

### Curation Rationale
[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization
[More Information Needed]

#### Who are the source language producers?
[More Information Needed]

### Annotations

#### Annotation process
[More Information Needed]

#### Who are the annotators?
[More Information Needed]

### Personal and Sensitive Information
[More Information Needed]

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## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]

### Discussion of Biases
[More Information Needed]

### Other Known Limitations
[More Information Needed]

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## Additional Information

### Dataset Curators
[More Information Needed]

### Licensing Information
[More Information Needed]

Be sure to check the license requirements for the particular data used (as noted in the [LILA BC Licensing Information Section](https://huggingface.co/datasets/society-ethics/lila_camera_traps#licensing-information)).
This particular compilation has been marked as dedicated to the public domain by applying the [CC0 Public Domain Waiver](https://creativecommons.org/publicdomain/zero/1.0/). However, images may be licensed under different terms (as noted above). 

### Citation Information
[More Information Needed]
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### Contributions

The [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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