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README.md

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+---
+license: mit
+task_categories:
+- image-classification
+- image-regression
+tags:
+- medical
+- retina
+- age-prediction
+- fundus-images
+size_categories:
+- 1K<n<10K
+---
+
+# Retina Age Analysis Dataset
+
+## Dataset Description
+
+This dataset contains **9,857 retinal fundus images** from **5,393 patients** for age prediction tasks.
+
+### Dataset Summary
+
+- **Task**: Age prediction from retinal fundus images
+- **Images**: 9,857 high-quality retinal images
+- **Patients**: 5,393 unique patients
+- **Age Range**: 5-97 years
+- **Image Format**: JPEG
+- **Average Image Size**: ~1 MB
+
+### Supported Tasks
+
+1. **Regression**: Predict continuous age (5-97 years)
+2. **Classification**: Predict age group (5 classes: pediatric, young adult, middle age, senior, elderly)
+
+### Data Splits
+
+| Split | Images | Patients | Percentage |
+|-------|--------|----------|------------|
+| Train | 6,902 | 3,775 | 70% |
+| Validation | 1,493 | 809 | 15% |
+| Test | 1,462 | 809 | 15% |
+
+**Note**: Split at patient level to prevent data leakage.
+
+### Age Distribution
+
+| Age Group | Age Range | Count | Percentage |
+|-----------|-----------|-------|------------|
+| Pediatric | 5-17 | 291 | 3.0% |
+| Young Adult | 18-39 | 1,447 | 14.7% |
+| Middle Age | 40-59 | 2,946 | 29.9% |
+| Senior | 60-74 | 3,484 | 35.3% |
+| Elderly | 75+ | 1,689 | 17.1% |
+
+### Dataset Structure
+
+```
+retina-age-analysis/
+├── images/           # 9,857 retinal fundus images
+│   ├── img00001.jpg
+│   ├── img00002.jpg
+│   └── ...
+│
+└── splits/           # Train/val/test split CSV files
+    ├── train.csv     # 6,902 samples
+    ├── val.csv       # 1,493 samples
+    └── test.csv      # 1,462 samples
+```
+
+### Data Fields
+
+Each CSV file contains:
+
+- `image_id`: Image filename (without extension)
+- `patient_id`: Unique patient identifier
+- `patient_age`: Age in years (target variable for regression)
+- `age_group_broad`: Age category name
+- `age_group_broad_numeric`: Age category index (0-4, target for classification)
+- `patient_sex`: Gender (1=Male, 2=Female)
+- `exam_eye`: Eye examined (1=Right, 2=Left)
+- `diabetic_retinopathy`: DR status (0=No, 1=Yes)
+- `camera`: Camera type used
+- Additional clinical features
+
+### Usage Example
+
+```python
+from datasets import load_dataset
+from PIL import Image
+import pandas as pd
+
+# Load dataset
+dataset = load_dataset("ramankamran/retina-age-analysis")
+
+# Load splits
+train_df = pd.read_csv("hf://datasets/ramankamran/retina-age-analysis/splits/train.csv")
+val_df = pd.read_csv("hf://datasets/ramankamran/retina-age-analysis/splits/val.csv")
+test_df = pd.read_csv("hf://datasets/ramankamran/retina-age-analysis/splits/test.csv")
+
+# Load an image
+from huggingface_hub import hf_hub_download
+img_path = hf_hub_download(
+    repo_id="ramankamran/retina-age-analysis",
+    filename="images/img00001.jpg",
+    repo_type="dataset"
+)
+image = Image.open(img_path)
+
+# Get corresponding label
+label = train_df[train_df['image_id'] == 'img00001']['patient_age'].values[0]
+```
+
+### PyTorch DataLoader
+
+See the training code in the repository for PyTorch DataLoader implementation with:
+- Data augmentation (rotation, flip, brightness, contrast)
+- ImageNet normalization
+- Batch loading
+
+### Baseline Results
+
+**Regression (Age Prediction):**
+- MAE: 7-10 years (baseline)
+- Target: < 5 years (optimized)
+
+**Classification (Age Groups):**
+- Accuracy: 70-75% (baseline)
+- Target: 85-90% (with semi-supervised learning)
+
+### License
+
+MIT License
+
+### Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{retina_age_analysis,
+  author = {Raman Kamran},
+  title = {Retina Age Analysis Dataset},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/ramankamran/retina-age-analysis}
+}
+```
+
+### Dataset Curators
+
+Dataset cleaned and prepared by ramankamran.
+
+### Preprocessing
+
+- Removed images with missing age labels (33.5% of original data)
+- Removed inadequate quality images (8.9%)
+- Verified all image files exist
+- Created stratified train/val/test splits
+- Patient-level splitting to prevent data leakage
+
+### Intended Use
+
+- Medical image analysis research
+- Age prediction from retinal images
+- Transfer learning for ophthalmology tasks
+- Semi-supervised learning experiments
+
+### Limitations
+
+- Class imbalance (elderly patients over-represented, pediatric under-represented)
+- Single imaging center data
+- Requires domain knowledge for clinical interpretation
+
+### Additional Information
+
+For training code and examples, see: [GitHub Repository](https://github.com/ramankamran/retina-age-analysis)