---
pretty_name: Wine Images Dataset 126K
tags:
  - wine
  - food-and-drink
  - image-classification
  - computer-vision
  - multimodal
task_categories:
  - image-classification
  - feature-extraction
  - image-to-text
size_categories:
  - 100K<n<1M
license: cc-by-4.0
language:
  - en
dataset_info:
  features:
    - name: image_id
      dtype: string
    - name: image
      dtype: image
    - name: wine_name
      dtype: string
  config_name: default
  splits:
    - name: train
      num_bytes: 6442450944
      num_examples: 107821
  download_size: 6442450944
  dataset_size: 6442450944
---

# Wine Images Dataset 126K

A comprehensive dataset of 107,821 wine bottle images linked to the [Wine Text Dataset 126K](https://huggingface.co/datasets/cipher982/wine-text-126k). This companion dataset provides high-quality wine bottle images for computer vision, multimodal machine learning, and wine recognition tasks.

## Dataset Description

This dataset contains wine bottle images scraped from wine retailer websites. Each image is linked to detailed wine information (descriptions, pricing, categories, regions) via stable IDs that connect to the companion text dataset.

### Key Features

- **107,821 wine bottle images** in high resolution
- **Stable linking** to companion text dataset via `image_id`
- **Clean naming**: Images named as `wine_XXXXXX.jpg` matching text dataset IDs
- **Quality images**: Average 57KB per image, various resolutions
- **Complete coverage**: 98% of wines from text dataset have corresponding images

## Dataset Structure

```python
{
  "image_id": "wine_000001",           # Links to cipher982/wine-text-126k
  "image": <PIL.Image>,                # Wine bottle image
  "wine_name": "Dom Perignon Vintage 2008"  # Wine name for reference
}
```

## Companion Dataset

This image dataset is designed to work with:
- **[cipher982/wine-text-126k](https://huggingface.co/datasets/cipher982/wine-text-126k)**: Text descriptions, prices, categories, regions

## Usage

### Basic Loading

```python
from datasets import load_dataset

# Load the image dataset
image_dataset = load_dataset("cipher982/wine-images-126k")

# Load the companion text dataset
text_dataset = load_dataset("cipher982/wine-text-126k")

# Access images and text
images = image_dataset["train"]
texts = text_dataset["train"]

# Example: Get image and text for same wine
wine_id = "wine_000001"
wine_image = images.filter(lambda x: x["image_id"] == wine_id)[0]["image"]
wine_text = texts.filter(lambda x: x["id"] == wine_id)[0]
```

### Multimodal Usage

```python
import pandas as pd
from datasets import load_dataset

# Load both datasets
images = load_dataset("cipher982/wine-images-126k")["train"]
texts = load_dataset("cipher982/wine-text-126k")["train"]

# Convert to DataFrames for easy joining
df_images = images.to_pandas().set_index('image_id')
df_texts = texts.to_pandas().set_index('id')

# Join datasets on wine ID
df_multimodal = df_texts.join(df_images, how='inner')

print(f"Multimodal dataset: {len(df_multimodal):,} wines with both text and images")

# Example: Access wine with both image and description
wine = df_multimodal.iloc[0]
print(f"Name: {wine['name']}")
print(f"Description: {wine['description'][:100]}...")
print(f"Price: ${wine['price']}")
wine['image'].show()  # Display the wine bottle image
```

### Computer Vision Tasks

```python
from datasets import load_dataset
import torch
from torchvision import transforms

# Load dataset
dataset = load_dataset("cipher982/wine-images-126k")["train"]

# Preprocessing for computer vision models
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                        std=[0.229, 0.224, 0.225])
])

# Process images
def preprocess(example):
    example["image"] = transform(example["image"])
    return example

dataset = dataset.map(preprocess)
```

## Data Quality

- **Image Count**: 107,821 wine bottle images
- **Coverage**: 98.0% of wines from text dataset have images
- **File Format**: JPEG images
- **Average Size**: 57KB per image
- **Total Size**: ~5.8GB
- **Naming**: Consistent `wine_XXXXXX.jpg` format
- **Quality**: High-resolution product photos from wine retailers

## Use Cases

### Computer Vision
- **Wine Classification**: Classify wines by bottle shape, label, region
- **Brand Recognition**: Identify wine producers from bottle images
- **Quality Assessment**: Analyze bottle condition and presentation
- **Object Detection**: Detect wine bottles in complex scenes

### Multimodal Learning
- **Image-Text Matching**: Match wine descriptions to bottle images
- **Caption Generation**: Generate wine descriptions from bottle images
- **Visual Question Answering**: Answer questions about wine bottles
- **Recommendation Systems**: Visual and textual wine recommendations

### Research Applications
- **Food & Beverage Analysis**: Study wine packaging and branding trends
- **Cultural Studies**: Analyze wine bottle design across regions
- **Marketing Research**: Study visual elements in wine presentation
- **Computer Vision Benchmarks**: Large-scale wine image classification

## Dataset Statistics

### Image Coverage by Region
| Region     | Images  | Coverage |
|------------|---------|----------|
| other      | 84,127  | 79.5%    |
| california | 10,687  | 98.2%    |
| france     | 4,753   | 98.2%    |
| italy      | 4,254   | 98.4%    |

### Image Coverage by Wine Category
| Category   | Images  | Coverage |
|------------|---------|----------|
| red_wine   | 60,893  | 97.9%    |
| other      | 29,732  | 97.5%    |
| white_wine | 25,701  | 97.9%    |
| rosé       | 2,658   | 98.0%    |
| dessert    | 2,469   | 98.0%    |
| sparkling  | 1,568   | 97.6%    |

## Ethical Considerations

- **Data Source**: Images collected from public wine retailer websites
- **Privacy**: No personal information in images
- **Commercial Use**: Please respect original retailers' intellectual property
- **Attribution**: Images represent retailer product photography
- **Quality**: Images reflect commercial wine presentation standards

## Technical Details

### File Organization
```
wine-images-126k/
├── images/
│   ├── wine_000000.jpg
│   ├── wine_000001.jpg
│   └── ... (107,821 images)
├── image_metadata.json
└── README.md
```

### Metadata Format
```json
{
  "image_id": "wine_000001",
  "filename": "wine_000001.jpg",
  "original_filename": "lmgmud1xsenlouwpzysc.jpg",
  "file_size": 47234
}
```

## Linking with Text Dataset

Images are linked to text data via stable `image_id` fields:

```python
# Text dataset (cipher982/wine-text-126k)
{
  "id": "wine_000001",
  "name": "Dom Perignon Vintage 2008",
  "description": "Complex champagne with...",
  "image_id": "wine_000001"  # Links to this dataset
}

# Image dataset (cipher982/wine-images-126k)
{
  "image_id": "wine_000001",  # Same ID links back to text
  "image": <wine bottle image>,
  "wine_name": "Dom Perignon Vintage 2008"
}
```

## Citation

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

```bibtex
@dataset{wine_images_126k,
  title={Wine Images Dataset 126K},
  author={David Rose},
  year={2025},
  url={https://huggingface.co/datasets/cipher982/wine-images-126k}
}
```

Also cite the companion text dataset:

```bibtex
@dataset{wine_text_126k,
  title={Wine Text Dataset 126K},
  author={David Rose},
  year={2025},
  url={https://huggingface.co/datasets/cipher982/wine-text-126k}
}
```

## License

This dataset is released under the **Creative Commons Attribution 4.0 International License (CC-BY-4.0)**.

**You are free to:**
- 🔄 **Share** — copy and redistribute the material in any medium or format
- 🔧 **Adapt** — remix, transform, and build upon the material for any purpose, even commercially

**Under the following terms:**
- 📝 **Attribution** — You must give appropriate credit and indicate if changes were made

**Data Collection Notice:**
The underlying wine bottle images were collected from publicly available retailer websites for research purposes under fair use. This dataset compilation, stable ID system, and organized structure represent our original contribution covered by this license.

Users should respect the intellectual property rights of the original wine bottle photography and retailer content.