---
tags:
- computer-vision
- audio
- keypoint-detection
- animal-behavior
- multi-modal
- jsonl
dataset_info:
  features:
  - name: bird_id
    dtype: string
  - name: back_bbox_2d
    sequence: float64
  - name: back_keypoints_2d
    sequence: float64
  - name: back_view_boundary
    sequence: int64
  - name: bird_name
    dtype: string
  - name: video_name
    dtype: string
  - name: frame_name
    dtype: string
  - name: frame_path
    dtype: image
  - name: keypoints_3d
    sequence:
      sequence: float64
  - name: radio_path
    dtype: binary
  - name: reprojection_error
    sequence: float64
  - name: side_bbox_2d
    sequence: float64
  - name: side_keypoints_2d
    sequence: float64
  - name: side_view_boundary
    sequence: int64
  - name: backpack_color
    dtype: string
  - name: experiment_id
    dtype: string
  - name: split
    dtype: string
  - name: top_bbox_2d
    sequence: float64
  - name: top_keypoints_2d
    sequence: float64
  - name: top_view_boundary
    sequence: int64
  - name: video_path
    dtype: video
  - name: acc_ch_map
    struct:
    - name: '0'
      dtype: string
    - name: '1'
      dtype: string
    - name: '2'
      dtype: string
    - name: '3'
      dtype: string
    - name: '4'
      dtype: string
    - name: '5'
      dtype: string
    - name: '6'
      dtype: string
    - name: '7'
      dtype: string
  - name: acc_sr
    dtype: float64
  - name: has_overlap
    dtype: bool
  - name: mic_ch_map
    struct:
    - name: '0'
      dtype: string
    - name: '1'
      dtype: string
    - name: '2'
      dtype: string
    - name: '3'
      dtype: string
    - name: '4'
      dtype: string
    - name: '5'
      dtype: string
    - name: '6'
      dtype: string
  - name: mic_sr
    dtype: float64
  - name: acc_path
    dtype: audio
  - name: mic_path
    dtype: audio
  - name: vocalization
    list:
    - name: overlap_type
      dtype: string
    - name: has_bird
      dtype: bool
    - name: 2ddistance
      dtype: bool
    - name: small_2ddistance
      dtype: float64
    - name: voc_metadata
      sequence: float64
  splits:
  - name: train
    num_bytes: 74517864701.0153
    num_examples: 6804
  - name: val
    num_bytes: 32619282428.19056
    num_examples: 2916
  - name: test
    num_bytes: 38018415640.55813
    num_examples: 3431
  download_size: 35456328366
  dataset_size: 145155562769.764
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
  - split: test
    path: data/test-*
---


# Bird3M Dataset

## Dataset Description

**Bird3M** is the first synchronized, multi-modal, multi-individual dataset designed for comprehensive behavioral analysis of freely interacting birds, specifically zebra finches, in naturalistic settings. It addresses the critical need for benchmark datasets that integrate precisely synchronized multi-modal recordings to support tasks such as 3D pose estimation, multi-animal tracking, sound source localization, and vocalization attribution. The dataset facilitates research in machine learning, neuroscience, and ethology by enabling the development of robust, unified models for long-term tracking and interpretation of complex social behaviors.

### Purpose
Bird3M bridges the gap in publicly available datasets for multi-modal animal behavior analysis by providing:
1. A benchmark for unified machine learning models tackling multiple behavioral tasks.
2. A platform for exploring efficient multi-modal information fusion.
3. A resource for ethological studies linking movement, vocalization, and social context to uncover neural and evolutionary mechanisms.

## Dataset Structure

The dataset is organized into three splits: `train`, `val`, and `test`, each as a Hugging Face `Dataset` object. Each row corresponds to a single bird instance in a video frame, with associated multi-modal data.

### Accessing Splits
```python
from datasets import load_dataset

dataset = load_dataset("anonymous-submission000/bird3m")
train_dataset = dataset["train"]
val_dataset = dataset["val"]
test_dataset = dataset["test"]
```

## Dataset Fields

Each example includes the following fields:

- **`bird_id`** (`string`): Unique identifier for the bird instance (e.g., "bird_1").
- **`back_bbox_2d`** (`Sequence[float64]`): 2D bounding box for the back view, format `[x_min, y_min, x_max, y_max]`.
- **`back_keypoints_2d`** (`Sequence[float64]`): 2D keypoints for the back view, format `[x1, y1, v1, x2, y2, v2, ...]`, where `v` is visibility (0: not labeled, 1: labeled but invisible, 2: visible).
- **`back_view_boundary`** (`Sequence[int64]`): Back view boundary, format `[x, y, width, height]`.
- **`bird_name`** (`string`): Biological identifier (e.g., "b13k20_f").
- **`video_name`** (`string`): Video file identifier (e.g., "BP_2020-10-13_19-44-38_564726_0240000").
- **`frame_name`** (`string`): Frame filename (e.g., "img00961.png").
- **`frame_path`** (`Image`): Path to the frame image (`.png`), loaded as a PIL Image.
- **`keypoints_3d`** (`Sequence[Sequence[float64]]`): 3D keypoints, format `[[x1, y1, z1], [x2, y2, z2], ...]`.
- **`radio_path`** (`binary`): Path to radio data (`.npz`), stored as binary.
- **`reprojection_error`** (`Sequence[float64]`): Reprojection errors for 3D keypoints.
- **`side_bbox_2d`** (`Sequence[float64]`): 2D bounding box for the side view.
- **`side_keypoints_2d`** (`Sequence[float64]`): 2D keypoints for the side view.
- **`side_view_boundary`** (`Sequence[int64]`): Side view boundary.
- **`backpack_color`** (`string`): Backpack tag color (e.g., "purple").
- **`experiment_id`** (`string`): Experiment identifier (e.g., "CopExpBP03").
- **`split`** (`string`): Dataset split ("train", "val", "test").
- **`top_bbox_2d`** (`Sequence[float64]`): 2D bounding box for the top view.
- **`top_keypoints_2d`** (`Sequence[float64]`): 2D keypoints for the top view.
- **`top_view_boundary`** (`Sequence[int64]`): Top view boundary.
- **`video_path`** (`Video`): Path to the video clip (`.mp4`), loaded as a Video object.
- **`acc_ch_map`** (`struct`): Maps accelerometer channels to bird identifiers.
- **`acc_sr`** (`float64`): Accelerometer sampling rate (Hz).
- **`has_overlap`** (`bool`): Indicates if accelerometer events overlap with vocalizations.
- **`mic_ch_map`** (`struct`): Maps microphone channels to descriptions.
- **`mic_sr`** (`float64`): Microphone sampling rate (Hz).
- **`acc_path`** (`Audio`): Path to accelerometer audio (`.wav`), loaded as an Audio signal.
- **`mic_path`** (`Audio`): Path to microphone audio (`.wav`), loaded as an Audio signal.
- **`vocalization`** (`list[struct]`): Vocalization events, each with:
  - `overlap_type` (`string`): Overlap/attribution confidence.
  - `has_bird` (`bool`): Indicates if attributed to a bird.
  - `2ddistance` (`bool`): Indicates if 2D keypoint distance is <20px.
  - `small_2ddistance` (`float64`): Minimum 2D keypoint distance (px).
  - `voc_metadata` (`Sequence[float64]`): Onset/offset times `[onset_sec, offset_sec]`.

## How to Use

### Loading and Accessing Data
```python
from datasets import load_dataset
import numpy as np

# Load dataset
dataset = load_dataset("anonymous-submission000/bird3m")
train_data = dataset["train"]

# Access an example
example = train_data[0]

# Access fields
bird_id = example["bird_id"]
keypoints_3d = example["keypoints_3d"]
top_bbox = example["top_bbox_2d"]
vocalizations = example["vocalization"]

# Load multimedia
image = example["frame_path"]  # PIL Image
video = example["video_path"]  # Video object
mic_audio = example["mic_path"]  # Audio signal
acc_audio = example["acc_path"]  # Audio signal

# Access audio arrays
mic_array = mic_audio["array"]
mic_sr = mic_audio["sampling_rate"]
acc_array = acc_audio["array"]
acc_sr = acc_audio["sampling_rate"]

# Load radio data
radio_bytes = example["radio_path"]
try:
    from io import BytesIO
    radio_data = np.load(BytesIO(radio_bytes))
    print("Radio data keys:", list(radio_data.keys()))
except Exception as e:
    print(f"Could not load radio data: {e}")

# Print example info
print(f"Bird ID: {bird_id}")
print(f"Number of 3D keypoints: {len(keypoints_3d)}")
print(f"Top Bounding Box: {top_bbox}")
print(f"Number of vocalization events: {len(vocalizations)}")

if vocalizations:
    first_vocal = vocalizations[0]
    print(f"First vocal event metadata: {first_vocal['voc_metadata']}")
    print(f"First vocal event overlap type: {first_vocal['overlap_type']}")
```

### Example: Extracting Vocalization Audio Clip
```python
if vocalizations and mic_sr:
    onset, offset = vocalizations[0]["voc_metadata"]
    onset_sample = int(onset * mic_sr)
    offset_sample = int(offset * mic_sr)
    vocal_audio_clip = mic_array[onset_sample:offset_sample]
    print(f"Duration of first vocal clip: {offset - onset:.3f} seconds")
    print(f"Shape of first vocal audio clip: {vocal_audio_clip.shape}")
```
**Code Availability**: Baseline code is available at [https://github.com/anonymoussubmission0000/bird3m](https://github.com/anonymoussubmission0000/bird3m).

## Citation
```bibtex
@article{2025bird3m,
  title={Bird3M: A Multi-Modal Dataset for Social Behavior Analysis Tool Building},
  author={tbd},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2025}
}
```