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README.md

@@ -1,58 +1,38 @@
+
 ---
 license: mit
 task_categories:
 - image-classification
 tags:
 - human-detection
+- face-classification
 - computer-vision
 size_categories:
-- n<1K
-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
+- 1K<n<10K
 ---
 
 # Human vs Non-Human Face Dataset
 
-This dataset is specifically curated for training binary classifiers to distinguish between real human faces and non-human faces (statues, art representations, etc.).
+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** |
 
-## 📁 Structure
-The dataset follows the standard ImageFolder/YOLO classification format:
-- `train/`: Training images
-- `val/`: Validation images
-- `test/`: Testing images (851 samples)
+## 📁 Format
+- Images are decodable as **PIL.Image** objects.
+- Labels: `0: human`, `1: non_human`.
 
-Classes: `human`, `non_human`
+## 🚀 Quick Start   
+```python
+from datasets import load_dataset
+ds = load_dataset("8Opt/human-nonhuman-face-classification")
 
-## 📊 Summary
-- **Human samples**: 433 (test set)
-- **Non-human samples**: 418 (test set)
-- **Format**: JPG/PNG images resized/cropped to face regions.
+# Access test set
+example = ds['test'][0]
+img, label = example['image'], example['label']
+img.show()