README.md

---
license: cc0-1.0
task_categories:
- video-classification
- object-detection
tags:
- baboons
- video
- animal behavior
- behavior recognition
- annotation
- annotated video
- conservation
- drone
- UAV
- imbalanced
- Kenya
- Mpala Research Centre
pretty_name: >-
  BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour
  Recognition from Drone Videos
description: >-
  BaboonLand is an aerial drone video dataset of wild olive baboons in Laikipia,
  Kenya, collected over 21 consecutive days across three troops. It contains 20+
  hours of footage with dense multi-individual scenes (up to ~70 baboons per
  frame) and annotations enabling detection, multi-object tracking, and behavior
  recognition.
size_categories:
- 1M<n<10M
language:
- en
configs:
- config_name: default
  data_files:
  - split: train
    path: "Dataset Viewer/train.csv"
  - split: validation
    path: "Dataset Viewer/val.csv"
---

# Dataset Card for BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos

## Dataset Description
- **Homepage:** [BaboonLand Site](https://baboonland.xyz/)
- **Repository:** https://huggingface.co/datasets/imageomics/BaboonLand
- **Paper:** [https://link.springer.com/article/10.1007/s11263-025-02493-5](https://link.springer.com/article/10.1007/s11263-025-02493-5)
- **arXiv:** [https://arxiv.org/pdf/2405.17698](https://arxiv.org/pdf/2405.17698)

### Dataset Summary
BaboonLand is an aerial drone video dataset of wild olive baboons (*Papio anubis*) collected over 21 consecutive days in Laikipia (Mpala Research Centre), Kenya, following three troops during morning and evening movements to and from sleeping sites. The dataset contains UAV footage across diverse environments (e.g., sleeping tree, river, rock, open savannah, cliff), with up to ~70 individuals per frame, yielding dense multi-object scenes from an overhead viewpoint. 

The dataset supports three core subtasks: detection, multi-object tracking, and behavior recognition. It includes (1) a detection dataset derived from ~5.3K-resolution frames via multi-scale tiling (≈30K images), (2) ~0.5 hours of dense tracking annotations, and (3) ~20 hours of behavior “mini-scenes” annotated into 12 behavior classes and additional category for occlusions.

### Download & Reconstruct

BaboonLand is stored as split ZIP archives (`*.zip.part.*`) tracked with **Git LFS**. You can either download everything at once, or pull only a specific subset (Charades / Dataset / Tracking), then **concatenate parts**.

**Integrity check:** Compare the printed `md5sum` values with the reference hashes in `BaboonLand/manifest.json`.

---

### Option A — Download everything (all parts)

```bash
git clone https://huggingface.co/datasets/imageomics/BaboonLand
cd BaboonLand

cat BaboonLand/charades.zip.part* > BaboonLand/charades.zip
cat BaboonLand/dataset.zip.part* > BaboonLand/dataset.zip
cat BaboonLand/tracking.zip.part* > BaboonLand/tracking.zip

md5sum BaboonLand/charades.zip
md5sum BaboonLand/dataset.zip
md5sum BaboonLand/tracking.zip

rm -rf BaboonLand/charades.zip.part*
rm -rf BaboonLand/dataset.zip.part*
rm -rf BaboonLand/tracking.zip.part*
```

---

### Option B — Download only Charades part

```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/imageomics/BaboonLand
cd BaboonLand

git lfs pull --include="BaboonLand/charades.zip.part*"
cat BaboonLand/charades.zip.part* > BaboonLand/charades.zip
md5sum BaboonLand/charades.zip
rm -rf BaboonLand/charades.zip.part*
```

---

### Option C — Download only Dataset part

```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/imageomics/BaboonLand
cd BaboonLand

git lfs pull --include="BaboonLand/dataset.zip.part*"
cat BaboonLand/dataset.zip.part* > BaboonLand/dataset.zip
md5sum BaboonLand/dataset.zip
rm -rf BaboonLand/dataset.zip.part*
```

---

### Option D — Download only Tracking part

```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/imageomics/BaboonLand
cd BaboonLand

git lfs pull --include="BaboonLand/tracking.zip.part*"
cat BaboonLand/tracking.zip.part* > BaboonLand/tracking.zip
md5sum BaboonLand/tracking.zip
rm -rf BaboonLand/tracking.zip.part*
```

---

### Option E — Download only CVAT templates

```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/imageomics/BaboonLand
cd BaboonLand

git lfs pull --include="BaboonLand/cvat_templates"
```

---

### Directory Layout

```text
BaboonLand
    /charades -> The dataset converted to Charades format to train and evaluate behavior
                 recognition models. You can download the generated dataset from our webpage
                 or you can generate it yourself.
        ...
    /cvat_templates -> Templates to backup projects in CVAT and explore/adjust annotations.
        /behavior.zip
        /tracking.zip
    /dataset
        /video_1
            /actions
                /0.xml
                /1.xml   -> Behavior annotations for individual with ID=1
                ...
                /n.xml
            /mini-scenes -> Mini-scenes generated from video.mp4 and tracks.xml
                /0.mp4
                /1.mp4
                ...
                /n.mp4
            /timeline.jpg
            /tracks.xml  -> Tracks + bounding boxes (CVAT for video 1.1). Each track has a unique ID.
            /video.mp4   -> Original drone video
        /video_2
            ...
        /video_n
            ...
    /scripts
        /requirements.txt
        /tracks2mini-scenes.py
        /dataset2charades.py
        /charades2video.py
        /charades2visual.py
        /dataset2tracking.py
        /tracking2ultralytics.py
        /ultralytics2pyramid.py
    /tracking -> Tracking split + (optionally) Ultralytics-format detection data.
        ...
    /README.md
```

### Supported Tasks and Leaderboards

#### Detection
We evaluate YOLOv8-X model with input resolution of 768x768 on our dataset and report mAP@50, Precision, and Recall:

| Model | mAP@50 | Precision | Recall |
| --- | ---: | ---: | ---: |
| YOLOv8-X | 92.62 | 93.70 | 87.60 |

#### Tracking
We evaluate SORT, DeepSORT, StrongSORT, ByteTrack, and BotSort tracking algorithms on our dataset and report MOTA, MOTP, IDF1, Precision, and Recall:

| Tracker | MOTA | MOTP | IDF1 | Precision | Recall |
| --- | ---: | ---: | ---: | ---: | ---: |
| SORT | 84.76 | 50.15 | 77.43 | 90.83 | 91.19 |
| DeepSORT | 84.40 | 87.22 | 81.38 | 90.26 | 91.57 |
| StrongSORT | 82.48 | 85.37 | 84.98 | 88.00 | 90.10 |
| ByteTrack | 63.55 | 34.10 | 77.01 | 96.32 | 64.90 |
| BotSort | 63.81 | 34.31 | 78.24 | 97.21 | 66.16 |

#### Behavior Classes:
- Walking/Running
- Sitting/Standing
- Fighting/Playing
- Self-Grooming
- Being Groomed
- Grooming Somebody
- Mutual Grooming
- Infant-Carrying
- Foraging
- Drinking
- Mounting
- Sleeping
- Occluded

#### Behavior Recognition
We evaluate I3D, SlowFast, and X3D models on our dataset and report Micro-Average (Per Instance) and Macro-Average (Per Class) accuracy.

| Method | WI | Micro Top-1 | Micro Top-3 | Micro Top-5 | Macro Top-1 | Macro Top-3 | Macro Top-5 |
| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |
| I3D | Random | 61.29 | 89.38 | 92.34 | 26.53 | 54.51 | 65.47 |
| SlowFast | Random | 61.71 | 90.35 | 93.11 | 27.08 | 56.73 | 67.61 |
| X3D | Random | 63.97 | 91.34 | 95.17 | 30.04 | 60.58 | 72.13 |
| X3D | K-400 | 64.89 | 92.54 | 96.66 | 31.41 | 62.04 | 74.01 |

### Languages

English

## Dataset Structure

BaboonLand provides original videos, CVAT-formatted annotations, derived mini-scenes, and scripts to generate task-specific training formats (e.g., Ultralytics/YOLO and Charades for SlowFast).

### Data Instances
Each `dataset/video_k/` directory contains:

- `video.mp4`: original UAV video
- `tracks.xml`: per-frame tracks (IDs + bounding boxes)
- `actions/*.xml`: per-track behavior labels (filename matches track ID)
- `mini-scenes/*.mp4`: cropped clips centered on each tracked individual (filename matches track ID)

### Data Fields
BaboonLand supports three derived tasks:

- **Detection:** bounding boxes for baboons (also convertible to Ultralytics/YOLO format via provided scripts).
- **Tracking:** per-frame tracks with persistent IDs and bounding boxes (stored in simplified CVAT for video 1.1).
- **Behavior recognition:** per-individual **mini-scenes** (cropped clips centered on each tracked individual) labeled into **12 behavior classes + Occluded**.

### Data Splits
BaboonLand includes task-specific evaluation sets:

- **Tracking:** 75% of each video for training, 25% for testing.
- **Detection (YOLO-formatted):** 80% training, 7% validation, 13% testing.
- **Behavior recognition (Charades format):** 75% training, 25% testing.

#### Data Collection and Procedures
- **Species:** Olive baboons (*Papio anubis*)
- **Location:** Mpala Research Centre, Laikipia County, Kenya
- **Capture:** DJI Air 2S, videos recorded in **5.3K**
- **Procedure:** all flights were conducted above 20 meters from a closes animal.

## Personal and Sensitive Information

- No humans can be distinguished in the videos.
- Data collection followed research licensing and animal care protocols (see Acknowledgments).

### Authors
- Isla Duporge
- Maksim Kholiavchenko
- Roi Harel
- Scott Wolf
- Daniel Rubenstein
- Meg Crofoot
- Tanya Berger-Wolf
- Stephen Lee
- Julie Barreau
- Jenna Kline
- Michelle Ramirez
- Charles Stewart

### Citation Information

#### Dataset

####
```
@misc{hdr_imageomics_institute_2026,
	author       = { Isla Duporge and Maksim Kholiavchenko and Roi Harel and Scott Wolf and Daniel Rubenstein and Tanya Berger-Wolf and Margaret Crofoot and Stephen Lee and Julie Barreau and Jenna Kline and Michelle Ramirez and Charles Stewart },
	title        = { BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos },
	year         = 2026,
	url          = { https://huggingface.co/datasets/imageomics/BaboonLand },
	doi          = { 10.57967/hf/7470 },
	publisher    = { Hugging Face }
}
```

#### Paper
```
@article{duporge2025baboonland,
  title={BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos},
  author={Duporge, Isla and Kholiavchenko, Maksim and Harel, Roi and Wolf, Scott and Rubenstein, Daniel I and Crofoot, Margaret C and Berger-Wolf, Tanya and Lee, Stephen J and Barreau, Julie and Kline, Jenna and others},
  journal={International Journal of Computer Vision},
  pages={1--12},
  year={2025},
  publisher={Springer}
}
```

### Contributions / Acknowledgments

This material is based upon work supported by the National Science Foundation under Award No. 2118240 and Award No. 2112606. ID was supported by the National Academy of Sciences Research Associate Program and the United States Army Research Laboratory while conducting this study. ID collected all the UAV data on a Civil Aviation Authority Drone License CAA NQE Approval Number: 0216/1365 in conjunction with authorization from a KCAA operator under a Remote Pilot License. The data was gathered at the Mpala Research Centre in Kenya, in accordance with Research License No. NACOSTI/P/22/18214. The data collection protocol adhered strictly to the guidelines set forth by the Institutional Animal Care and Use Committee under permission No. IACUC 1835F.