---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Adalar
          '1': Arnavutköy
          '2': Ataşehir
          '3': Avcılar
          '4': Bahçelievler
          '5': Bakırköy
          '6': Bayrampaşa
          '7': Bağcılar
          '8': Başakşehir
          '9': Beykoz
          '10': Beylikdüzü
          '11': Beyoğlu
          '12': Beşiktaş
          '13': Büyükçekmece
          '14': Esenler
          '15': Esenyurt
          '16': Eyüpsultan
          '17': Fatih
          '18': Gaziosmanpaşa
          '19': Güngören
          '20': Kadıköy
          '21': Kartal
          '22': Kâğıthane
          '23': Küçükçekmece
          '24': Maltepe
          '25': Pendik
          '26': Sancaktepe
          '27': Sarıyer
          '28': Silivri
          '29': Sultanbeyli
          '30': Sultangazi
          '31': Tuzla
          '32': Zeytinburnu
          '33': Çatalca
          '34': Çekmeköy
          '35': Ümraniye
          '36': Üsküdar
          '37': Şile
          '38': Şişli
  - name: district
    dtype:
      class_label:
        names:
          '0': Adalar
          '1': Arnavutköy
          '2': Ataşehir
          '3': Avcılar
          '4': Bahçelievler
          '5': Bakırköy
          '6': Bayrampaşa
          '7': Bağcılar
          '8': Başakşehir
          '9': Beykoz
          '10': Beylikdüzü
          '11': Beyoğlu
          '12': Beşiktaş
          '13': Büyükçekmece
          '14': Esenler
          '15': Esenyurt
          '16': Eyüpsultan
          '17': Fatih
          '18': Gaziosmanpaşa
          '19': Güngören
          '20': Kadıköy
          '21': Kartal
          '22': Kâğıthane
          '23': Küçükçekmece
          '24': Maltepe
          '25': Pendik
          '26': Sancaktepe
          '27': Sarıyer
          '28': Silivri
          '29': Sultanbeyli
          '30': Sultangazi
          '31': Tuzla
          '32': Zeytinburnu
          '33': Çatalca
          '34': Çekmeköy
          '35': Ümraniye
          '36': Üsküdar
          '37': Şile
          '38': Şişli
  splits:
  - name: train
    num_bytes: 6816258966.844
    num_examples: 83206
  - name: test
    num_bytes: 1783120212.128
    num_examples: 20818
  download_size: 8861913293
  dataset_size: 8599379178.972
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# Istanbul Districts Image Dataset

A comprehensive dataset of 104,024 street-level images from 39 districts of Istanbul, Turkey. This dataset is designed for machine learning and computer vision applications, particularly for location classification tasks.

## Dataset Overview

- **Total Images:** 104,024
- **Districts:** 39 districts of Istanbul
- **Split:** 80% training (~83,000 images), 20% testing (~21,000 images)
- **Top 5 Districts by Image Count:**
  - Fatih: 13,103 images
  - Beşiktaş: 11,155 images
  - Eyüpsultan: 10,810 images
  - Üsküdar: 8,993 images
  - Kâğıthane: 8,978 images

## Features

Each image in the dataset includes:
- `image`: The image file
- `label`: District category (0-38)
- `district`: District name as string

## Usage

Load the dataset using the Hugging Face Datasets library:

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("your-username/repository-name")

# Access train and test sets
train_dataset = dataset["train"]
test_dataset = dataset["test"]

# Access an example
image = train_dataset[0]["image"]
district = train_dataset[0]["district"]
```

## Sample Model Training

Here's a quick example using Vision Transformer for district classification:

```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from datasets import load_dataset

# Load dataset
dataset = load_dataset("your-username/repository-name")

# Image processor and model
processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
model = AutoModelForImageClassification.from_pretrained(
    "google/vit-base-patch16-224", 
    num_labels=39,
    id2label={i: district for i, district in enumerate(dataset["train"].features["district"].names)}
)

# Prepare dataset
def preprocess_data(examples):
    return processor(images=examples["image"], return_tensors="pt")

train_dataset = dataset["train"].map(preprocess_data, batched=True)
# Continue with model training...
```

## Source

This dataset contains street-level images collected from various districts of Istanbul, Turkey. The images were gathered and processed for use in the GeoGuessr project.

## License

This dataset is available for educational and research purposes. For commercial use, please contact the author.

## Citation

If you use this dataset in your research, please cite:

```
@dataset{istanbul_districts_2023,
  author    = {EREN FAZLIOĞLU},
  title     = {Istanbul Districts Image Dataset},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/erenfazlioglu}
}
```