Datasets:
File size: 1,499 Bytes
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license: mit
task_categories:
- image-classification
tags:
- human-detection
- face-classification
- computer-vision
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': human
'1': non_human
splits:
- name: train
num_bytes: 58130594.868
num_examples: 5973
- name: validation
num_bytes: 17288633.048
num_examples: 1706
- name: test
num_bytes: 9426595.0
num_examples: 855
download_size: 88469762
dataset_size: 84845822.91600001
---
# Human vs Non-Human Face Dataset
A robust dataset for binary classification between real human faces and non-human face-like objects (statues, art, gaming, anime).
## 📊 Dataset Statistics
| Split | Human | Non-Human | Total |
| :--- | :--- | :--- | :--- |
| **Train** | 3,024 | 2,949 | 5,973 |
| **Validation** | 864 | 842 | 1,706 |
| **Test** | 433 | 422 | 855 |
| **Total** | | | **8,534** |
## 📁 Format
- Images are decodable as **PIL.Image** objects.
- Labels: `0: human`, `1: non_human`.
## 🚀 Quick Start
```python
from datasets import load_dataset
ds = load_dataset("8Opt/human-nonhuman-face-classification")
# Access test set
example = ds['test'][0]
img, label = example['image'], example['label']
img.show()
|