Update README.md

README.md

@@ -11,35 +11,86 @@
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-# **Dataset Description**
 # <span style="font-size: 1.5em; font-weight: bold;">Dataset Description</span>
-This COCO dataset is designed for real-time "precise eye detection" under various conditions. It contains highly accurate bounding box annotations, manually retraced to ensure maximum precision.
+This COCO dataset is designed for real-time "precise eye detection" under various conditions. It contains highly accurate bounding box annotations, manually retraced to ensure maximum precision.  
+## **Dataset Splits**
+This dataset is intentionally provided as a **single training split** containing all 72,317 examples. This design choice allows researchers to:  
 
+- Create custom split ratios tailored to their specific needs  
+- Ensure different random seeds don't lead to overlapping examples between studies  
+- Implement cross-validation strategies more flexibly  
+
+You are responsible for creating appropriate validation and test splits using the provided training data. We recommend using 70-80% for training, 10-15% for validation, and 10-15% for testing, depending on your requirements.
 
-# **Loading the Dataset with Hugging Face 🤗**
-This dataset is in COCO format and can be easily loaded using the <u>datasets library</u> distributed by HuggingFace.  
 
-**Important Note on Images**:  
-The images are provided in a compressed <u>ZIP file</u> (**images.zip**). The load_dataset function will automatically handle the extraction for you. You do not need to extract them manually.  
 
-## **Example Code**:  
-from datasets import load_dataset  
-\# Load the dataset (automatically extracts images from the zip file)  
-dataset = load_dataset("AndreaPorri/Eyes-Detection")  
+# <span style="font-size: 1.5em; font-weight: bold;">Loading the Dataset with Hugging Face 🤗</span>
+This dataset is in COCO format and can be easily loaded using the <u>datasets library</u> distributed by HuggingFace.  
 
-\# You can now access the training split  
-train_dataset = dataset['train']
+**Important Notes:**  
+- **Single Split:** This dataset contains only a **training split** (`train`). You will need to create your own validation and test splits programmatically.
+- **Images:** The images are provided in a compressed ZIP file (`images.zip`). The `load_dataset` function will automatically handle the extraction for you.
+
+## **Example Code**: 
+```python
+from datasets import load_dataset
+from sklearn.model_selection import train_test_split
+import numpy as np
+
+# Load the entire dataset as the training split
+dataset = load_dataset("AndreaPorri/Eyes-Detection")
+full_train_dataset = dataset['train']  # This contains all 72,317 examples
+
+# It is NECESSARY to create a three-part division (training/validation/testing, to obtain and verify results reliably) and keep the test set completely separate from other data during model development and hyperparameter tuning.
+# First split: 80% train, 20% temp (for val+test)
+first_split = full_train_dataset.train_test_split(test_size=0.2, seed=42)
+
+# Second split: split temp into 50% validation, 50% test (10% each of total)
+second_split = first_split['test'].train_test_split(test_size=0.5, seed=42)
+
+final_dataset = {
+    'train': first_split['train'],
+    'validation': second_split['train'], 
+    'test': second_split['test']
+}
+ 
+```  
+
+**Note**: When using <u>load_dataset</u>, the extracted images will be cached in the ~/.cache/huggingface/datasets directory, creating the standard folder structure expected by the **COCO loader**.  
+
+## **For Advanced Usage (Manual Extraction or COCO Tools):**
+If you prefer to handle the extraction manually or use the annotations directly with other libraries:    
+1. <u>Manual Extraction</u>:  
+Download and extract images.zip to a folder. You can then use the data_dir parameter:
+```python
+dataset = load_dataset("AndreaPorri/Eyes-Detection", data_dir="path/to/extracted_images")
+```  
+2. <u>Using COCO Annotations Directly</u>:
+The annotations.coco.json file follows the standard COCO format and can be used with libraries like torchvision or pycocotools.  
+```python
+from torchvision.datasets import CocoDetection
+import torchvision.transforms as transforms
+
+coco_dataset = CocoDetection(
+    root='path/to/extracted_images',
+    annFile='path/to/annotations.coco.json',
+    transform=transforms.ToTensor()
+)
+```  
   
 
-## **Key features**
+# <span style="font-size: 1.5em; font-weight: bold;">Key features</span>
 - 72,317 images of close-up eyes, people's faces, and real-life scenes.
 - All bounding boxes have been **completely re-annotated manually** on Roboflow to ensure maximum accuracy.
+- Annotations provided in ready-to-use COCO format for easy integration with most object detection frameworks.
+- **Single Training Split:** The dataset is provided as a single training split to allow maximum flexibility in creating custom train/validation/test splits for different research needs.
 - <u>Various Image acquisition conditions</u>:
    - Single person (1 or 2 eyes), whole people, groups, ...
    - Open and closed eyes.
    - Diversity of lighting and poses.
 
-## **Preprocessing e Augmentation applied**
+
+# <span style="font-size: 1.5em; font-weight: bold;">Preprocessing e Augmentation applied</span>
 
 1. **<u>Preprocessing</u>**:
    - Auto-orientation (with EXIF orientation stripping)
@@ -56,17 +107,22 @@ train_dataset = dataset['train']
    - Cutout: 7 boxes, each with a size of 2%
 
 
-## **Dataset Structures**
-Eyes-Detection dataset/  
-    ├── README.md  
-    ├── Images  
-    ├── annotations.json  
-    ├── dataset_info.json  
-    ├── README.dataset.txt  
-    ├── LICENSE  
+# <span style="font-size: 1.5em; font-weight: bold;">Repository File Structure</span>
+The repository contains the following files. The standard structure for a COCO dataset on Hugging Face is automatically created when loaded.
+
+```text
+Eyes-Detection/
+├── README.md                 # This dataset card
+├── dataset_info.json         # Dataset metadata for Hugging Face
+├── annotations.coco.json     # Annotations in COCO format
+├── images.zip                # All images in a single compressed archive
+├── README.dataset.txt        # Marginal info
+└── LICENSE                   # The CC BY-NC-SA 4.0 license file
+``` 
+
 
 
-## **License - CC BY-NC-SA 4.0**
+# <span style="font-size: 1.5em; font-weight: bold;">License - CC BY-NC-SA 4.0</span>
 
 1. **<u>What you can do</u>**:
    - Share - copy and redistribute the material
@@ -82,30 +138,36 @@ Eyes-Detection dataset/
    "Accurate Eyes Detection Dataset" by AndreaPorri is licensed under CC BY-NC-SA 4.0  
    Dataset source: https://huggingface.co/datasets/AndreaPorri/Eyes-Detection
 
-## **Expected Uses**
+
+# <span style="font-size: 1.5em; font-weight: bold;">Expected Uses</span>  
 - Real-time eye detection.
 - Part of an Eye-Tracking pipeline for computer vision applications.
 - Academic research.
 - Research for medical applications.
 
-## **Limitations and Ethical Considerations**
+
+# <span style="font-size: 1.5em; font-weight: bold;">Limitations and Ethical Considerations</span>  
 - The dataset contains <u>images of faces</u>
 - Use permitted <u>only for non-commercial purposes</u>
 - It is the user's responsibility to ensure ethical use in compliance with privacy laws.
 
-## **Original Datasets of Origin**
+
+# <span style="font-size: 1.5em; font-weight: bold;">Original Datasets of Origin</span>  
 This dataset was created based on:
 1. **EyeCon Dataset** - https://app.roboflow.com/andreap/eyecon-eaux0-oykgj/1
 2. **Eyes Detection Dataset** - https://app.roboflow.com/andreap/eyes_detection-bupne/1
 
-## **Disclaimer**
-The user is responsible for the ethical and legal use of this dataset. The creator assumes no responsibility for any improper use.
 
-## **Contacts**
+# <span style="font-size: 1.5em; font-weight: bold;">Disclaimer</span>  
+The user is responsible for the ethical and legal use of this dataset. <u>The creator assumes no responsibility for any improper use</u>.
+
+
+# <span style="font-size: 1.5em; font-weight: bold;">Contacts</span>
 For questions or additional information:
 - Email: andrea.porri@student.unisi.it
 
-## **Citazione**
+
+# <span style="font-size: 1.5em; font-weight: bold;">Citation</span>
 If you use this dataset in your research, please cite it as:
 
 ```bibtex