---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: image
    dtype: image
  - name: height
    dtype: float64
  - name: weight
    dtype: float64
  - name: gender
    dtype: int64
  - name: age
    dtype: int64
  splits:
  - name: train
    num_bytes: 1347895106
    num_examples: 6487
  - name: test
    num_bytes: 162397956
    num_examples: 721
  download_size: 1587814198
  dataset_size: 1510293062
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: apache-2.0
task_categories:
  - image-classification
  - image-feature-extraction
tags:
  - human-body
  - biometrics
  - age-estimation
  - height-estimation
  - weight-estimation
  - gender-classification
  - celebrity
---
# Celeb-FBI: Celebrity Full Body Images Dataset

A cleaned and restructured version of the Celeb-FBI dataset containing 7,208 full-body celebrity images with annotations for height, weight, age, and gender.

## Dataset Description

This dataset consists of worldwide celebrity images captured in standing, front-facing positions. It is designed for research on human attribute estimation from full-body images, including height, weight, age, and gender prediction tasks.

### Dataset Structure

```
DatasetDict({
    train: Dataset({
        features: ['id', 'image', 'height', 'weight', 'gender', 'age'],
        num_rows: 6487
    })
    test: Dataset({
        features: ['id', 'image', 'height', 'weight', 'gender', 'age'],
        num_rows: 721
    })
})
```

### Features

| Feature  | Type    | Description                                      |
|----------|---------|--------------------------------------------------|
| `id`     | int     | Unique identifier for the image                  |
| `image`  | Image   | Full-body celebrity photograph                   |
| `height` | float   | Height in centimeters (-1 if missing/invalid)    |
| `weight` | float   | Weight in kilograms (-1 if missing/invalid)      |
| `gender` | int     | 0 = Male, 1 = Female                             |
| `age`    | int     | Age in years (-1 if missing/invalid)             |

### Statistics

| Attribute | Min   | Max    | Mean  | Valid Samples |
|-----------|-------|--------|-------|---------------|
| Height    | 79 cm | 259 cm | 170 cm | ~6,100       |
| Weight    | 38 kg | 202 kg | 66 kg  | ~5,300       |
| Age       | 14    | 97     | 42     | ~6,500       |
| Gender    | —     | —      | 61% F  | 7,208        |

## Data Processing

This version of the dataset includes several improvements over the original:

**Cleaning steps applied:**
- Converted height from feet to centimeters for standardization
- Removed implausible values (e.g., heights outside reasonable human range)
- Missing or invalid values are encoded as `-1`
- Fixed typos in original annotations
- Manual corrections for identified mislabeled samples

**Train/test split:**
- Stratified 90/10 split based on height, age, weight buckets, and gender
- Ensures balanced representation across attribute combinations

**Note:** Approximately 14% of samples have at least one missing or invalid attribute value (marked as -1). The dataset contains some noise in annotations—users should account for this in their applications.

## Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("alecccdd/celeb-fbi")

# Access training data
train_data = dataset["train"]

# Example: iterate over samples
for sample in train_data:
    image = sample["image"]
    height = sample["height"]  # in cm, -1 if missing
    weight = sample["weight"]  # in kg, -1 if missing
    gender = sample["gender"]  # 0=male, 1=female
    age = sample["age"]        # -1 if missing
    
# Filter valid samples for a specific attribute
valid_height_samples = train_data.filter(lambda x: x["height"] != -1)
```

## Intended Uses

- Human attribute estimation research (height, weight, age, gender)
- Multi-task learning on human body images
- Benchmarking computer vision models for biometric prediction
- Study of visual cues for physical attribute estimation

## Limitations

- Images are of celebrities and may not represent the general population
- Annotation accuracy depends on publicly available biographical data
- Some noise exists in the annotations; manual corrections were applied where identified but the dataset is not exhaustively verified
- Limited age range representation at extremes (few samples under 20 or over 80)
- Height and weight distributions may reflect celebrity demographics

## Ethical Considerations

This dataset uses publicly available images of celebrities. Users should be mindful of:
- Privacy implications when developing attribute estimation systems
- Potential biases in celebrity image datasets
- Responsible use in downstream applications

## Citation

If you use this dataset, please cite the original paper:

```bibtex
@misc{debnath2024celebfbibenchmarkdatasethuman,
      title={Celeb-FBI: A Benchmark Dataset on Human Full Body Images and Age, Gender, Height and Weight Estimation using Deep Learning Approach}, 
      author={Pronay Debnath and Usafa Akther Rifa and Busra Kamal Rafa and Ali Haider Talukder Akib and Md. Aminur Rahman},
      year={2024},
      eprint={2407.03486},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.03486}, 
}
```

**Paper:** [arXiv:2407.03486](https://arxiv.org/abs/2407.03486)