Add files using upload-large-folder tool

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Virtual environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# HuggingFace Hub
+.huggingface/
+
+# Temporary files
+*.tmp
+*.temp
+
+# Processing scripts (keep dataset files only)
+process_*.py
+create_*.py
+upload_*.py
+
+# Large working directories
+temp/
+work/
+staging/

LICENSE

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+Creative Commons Attribution 4.0 International License
+
+Wine Images Dataset 126K
+Copyright (c) 2025 David Rose
+
+This work is licensed under the Creative Commons Attribution 4.0 International License.
+To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
+or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+===============================================================================
+
+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.
+
+The licensor cannot revoke these freedoms as long as you follow the license terms.
+
+-------------------------------------------------------------------------------
+
+Under the following terms:
+
+    Attribution — You must give appropriate credit, provide a link to the license,
+                  and indicate if changes were made. You may do so in any reasonable
+                  manner, but not in any way that suggests the licensor endorses you
+                  or your use.
+
+    No additional restrictions — You may not apply legal terms or technological
+                                measures that legally restrict others from doing
+                                anything the license permits.
+
+-------------------------------------------------------------------------------
+
+Notices:
+
+You do not have to comply with the license for elements of the material in the
+public domain or where your use is permitted by an applicable exception or limitation.
+
+No warranties are given. The license may not give you all of the permissions
+necessary for your intended use. For example, other rights such as publicity,
+privacy, or moral rights may limit how you use the material.
+
+===============================================================================
+
+DATA COLLECTION NOTICE:
+
+The underlying wine bottle images were collected from publicly available retailer
+websites for research purposes. The dataset compilation, data cleaning, stable
+ID system, organized file structure, and metadata represent the original
+contribution covered by this license.
+
+Users should respect the intellectual property rights of the original wine
+bottle photography and retailer content. This dataset is intended for research,
+education, and fair use applications.

README.md

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+---
+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.

sample_100/metadata.json

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+[
+  {
+    "image_id": "wine_000000",
+    "filename": "wine_000000.jpg",
+    "original_filename": "lmgmud1xsenlouwpzysc.jpg",
+    "file_size": 311867
+  },
+  {
+    "image_id": "wine_000001",
+    "filename": "wine_000001.jpg",
+    "original_filename": "iqec7e0nf6mzyof6w1py.jpg",
+    "file_size": 47911
+  },
+  {
+    "image_id": "wine_000002",
+    "filename": "wine_000002.jpg",
+    "original_filename": "10521.jpg",
+    "file_size": 144696
+  },
+  {
+    "image_id": "wine_000003",
+    "filename": "wine_000003.jpg",
+    "original_filename": "aqi87aqobd3zc56au9hk.jpg",
+    "file_size": 51542
+  },
+  {
+    "image_id": "wine_000004",
+    "filename": "wine_000004.jpg",
+    "original_filename": "qajrqr4d6ttn2pffxczg.jpg",
+    "file_size": 19027
+  },
+  {
+    "image_id": "wine_000005",
+    "filename": "wine_000005.jpg",
+    "original_filename": "167055.jpg",
+    "file_size": 105880
+  },
+  {
+    "image_id": "wine_000006",
+    "filename": "wine_000006.jpg",
+    "original_filename": "gqzgrgsaxdcksx3noneh.jpg",
+    "file_size": 230723
+  },
+  {
+    "image_id": "wine_000009",
+    "filename": "wine_000009.jpg",
+    "original_filename": "z9znurwvr1olefnu5bvc.jpg",
+    "file_size": 107192
+  },
+  {
+    "image_id": "wine_000010",
+    "filename": "wine_000010.jpg",
+    "original_filename": "grol1gtorlqk1dzf0ebi.jpg",
+    "file_size": 482113
+  },
+  {
+    "image_id": "wine_000011",
+    "filename": "wine_000011.jpg",
+    "original_filename": "112580h.jpg",
+    "file_size": 85618
+  },
+  {
+    "image_id": "wine_000012",
+    "filename": "wine_000012.jpg",
+    "original_filename": "qohvf75rqxzgjw8dogxo.jpg",
+    "file_size": 456826
+  },
+  {
+    "image_id": "wine_000013",
+    "filename": "wine_000013.jpg",
+    "original_filename": "h2pvrjqiqbhc6ej7erg8.jpg",
+    "file_size": 19610
+  },
+  {
+    "image_id": "wine_000015",
+    "filename": "wine_000015.jpg",
+    "original_filename": "cbarqv7uymbkqmzhpfk5.jpg",
+    "file_size": 104832
+  },
+  {
+    "image_id": "wine_000016",
+    "filename": "wine_000016.jpg",
+    "original_filename": "xi45jq1u46sndkxdpdwd.jpg",
+    "file_size": 307444
+  },
+  {
+    "image_id": "wine_000017",
+    "filename": "wine_000017.jpg",
+    "original_filename": "rjnqcoe4uvz2mgqe2vtu.jpg",
+    "file_size": 207856
+  },
+  {
+    "image_id": "wine_000018",
+    "filename": "wine_000018.jpg",
+    "original_filename": "fz2u5mmxfax7m8oynksr.jpg",
+    "file_size": 45785
+  },
+  {
+    "image_id": "wine_000019",
+    "filename": "wine_000019.jpg",
+    "original_filename": "hlihos7ijibvtb9llqs4.jpg",
+    "file_size": 268601
+  },
+  {
+    "image_id": "wine_000020",
+    "filename": "wine_000020.jpg",
+    "original_filename": "da9lwssf17yjavczydwv.jpg",
+    "file_size": 308586
+  },
+  {
+    "image_id": "wine_000021",
+    "filename": "wine_000021.jpg",
+    "original_filename": "zkn4za760qqiepw7ns94.jpg",
+    "file_size": 94266
+  },
+  {
+    "image_id": "wine_000022",
+    "filename": "wine_000022.jpg",
+    "original_filename": "wvmdzyhxgfy9pocbmjvg.jpg",
+    "file_size": 88585
+  },
+  {
+    "image_id": "wine_000023",
+    "filename": "wine_000023.jpg",
+    "original_filename": "ph40c4utpp8hw4gijtvj.jpg",
+    "file_size": 17535
+  },
+  {
+    "image_id": "wine_000024",
+    "filename": "wine_000024.jpg",
+    "original_filename": "ic8osv7jkgzwofwlptdr.jpg",
+    "file_size": 95069
+  },
+  {
+    "image_id": "wine_000025",
+    "filename": "wine_000025.jpg",
+    "original_filename": "120000h.jpg",
+    "file_size": 82717
+  },
+  {
+    "image_id": "wine_000026",
+    "filename": "wine_000026.jpg",
+    "original_filename": "ixb51qv3ums9t2oeuclm.jpg",
+    "file_size": 85939
+  },
+  {
+    "image_id": "wine_000027",
+    "filename": "wine_000027.jpg",
+    "original_filename": "dqj2chpas9ybugtmawvd.jpg",
+    "file_size": 53470
+  },
+  {
+    "image_id": "wine_000028",
+    "filename": "wine_000028.jpg",
+    "original_filename": "zl4sifpsw0xxyrsg9h9g.jpg",
+    "file_size": 99575
+  },
+  {
+    "image_id": "wine_000029",
+    "filename": "wine_000029.jpg",
+    "original_filename": "s5w9f7zme42obc44tgma.jpg",
+    "file_size": 457841
+  },
+  {
+    "image_id": "wine_000030",
+    "filename": "wine_000030.jpg",
+    "original_filename": "dy841dnknwdk0bw3bci8.jpg",
+    "file_size": 89955
+  },
+  {
+    "image_id": "wine_000031",
+    "filename": "wine_000031.jpg",
+    "original_filename": "fzy4fbqyzfqyvwx2hzrp.jpg",
+    "file_size": 258068
+  },
+  {
+    "image_id": "wine_000032",
+    "filename": "wine_000032.jpg",
+    "original_filename": "ti9gzwupf0seb3mvited.jpg",
+    "file_size": 2501267
+  },
+  {
+    "image_id": "wine_000033",
+    "filename": "wine_000033.jpg",
+    "original_filename": "phccr0zduqot56ftmucz.jpg",
+    "file_size": 191201
+  },
+  {
+    "image_id": "wine_000035",
+    "filename": "wine_000035.jpg",
+    "original_filename": "znzhqgu4wioeec2l3j9e.jpg",
+    "file_size": 225936
+  },
+  {
+    "image_id": "wine_000036",
+    "filename": "wine_000036.jpg",
+    "original_filename": "iw0bqk0ewyrz2gs9uxb4.jpg",
+    "file_size": 69129
+  },
+  {
+    "image_id": "wine_000037",
+    "filename": "wine_000037.jpg",
+    "original_filename": "194469.jpg",
+    "file_size": 74079
+  },
+  {
+    "image_id": "wine_000038",
+    "filename": "wine_000038.jpg",
+    "original_filename": "x0l6f1dsbtbjb4zohcgc.jpg",
+    "file_size": 255015
+  },
+  {
+    "image_id": "wine_000040",
+    "filename": "wine_000040.jpg",
+    "original_filename": "olqfisyol6drhofzaf25.jpg",
+    "file_size": 193771
+  },
+  {
+    "image_id": "wine_000041",
+    "filename": "wine_000041.jpg",
+    "original_filename": "whyxgfxvefu66dmfclw2.jpg",
+    "file_size": 160383
+  },
+  {
+    "image_id": "wine_000042",
+    "filename": "wine_000042.jpg",
+    "original_filename": "wrbsylvzsvpdev0ea336.jpg",
+    "file_size": 38861
+  },
+  {
+    "image_id": "wine_000043",
+    "filename": "wine_000043.jpg",
+    "original_filename": "ffhbww9uctopqokazdwx.jpg",
+    "file_size": 90201
+  },
+  {
+    "image_id": "wine_000045",
+    "filename": "wine_000045.jpg",
+    "original_filename": "355416h.jpg",
+    "file_size": 83725
+  },
+  {
+    "image_id": "wine_000046",
+    "filename": "wine_000046.jpg",
+    "original_filename": "isdizbnl3ncytc1ii4rv.jpg",
+    "file_size": 95342
+  },
+  {
+    "image_id": "wine_000047",
+    "filename": "wine_000047.jpg",
+    "original_filename": "hs07erw0hdgvrhazgc1n.jpg",
+    "file_size": 24965
+  },
+  {
+    "image_id": "wine_000048",
+    "filename": "wine_000048.jpg",
+    "original_filename": "uzbp36yn2uymcczn5fxh.jpg",
+    "file_size": 98390
+  },
+  {
+    "image_id": "wine_000049",
+    "filename": "wine_000049.jpg",
+    "original_filename": "134638h.jpg",
+    "file_size": 35177
+  },
+  {
+    "image_id": "wine_000050",
+    "filename": "wine_000050.jpg",
+    "original_filename": "oofd2s5r6u03oqno4sn6.jpg",
+    "file_size": 371668
+  },
+  {
+    "image_id": "wine_000051",
+    "filename": "wine_000051.jpg",
+    "original_filename": "l27onbde2zljlqb2nyit.jpg",
+    "file_size": 63819
+  },
+  {
+    "image_id": "wine_000052",
+    "filename": "wine_000052.jpg",
+    "original_filename": "aqtji6meozrrxnsgwhls.jpg",
+    "file_size": 234844
+  },
+  {
+    "image_id": "wine_000053",
+    "filename": "wine_000053.jpg",
+    "original_filename": "uzdrteuq2jq40qjropoq.jpg",
+    "file_size": 35181
+  },
+  {
+    "image_id": "wine_000054",
+    "filename": "wine_000054.jpg",
+    "original_filename": "pbbiwcinbaar2lawyjht.jpg",
+    "file_size": 30494
+  },
+  {
+    "image_id": "wine_000055",
+    "filename": "wine_000055.jpg",
+    "original_filename": "cdzykxr3eixisklblp0i.jpg",
+    "file_size": 40198
+  },
+  {
+    "image_id": "wine_000056",
+    "filename": "wine_000056.jpg",
+    "original_filename": "pm8b5r2qj16gcree6nee.jpg",
+    "file_size": 37675
+  },
+  {
+    "image_id": "wine_000057",
+    "filename": "wine_000057.jpg",
+    "original_filename": "kjxae0aiumsm8pzzxw72.jpg",
+    "file_size": 80766
+  },
+  {
+    "image_id": "wine_000058",
+    "filename": "wine_000058.jpg",
+    "original_filename": "furwmsxdowl0r6vtrzsb.jpg",
+    "file_size": 87294
+  },
+  {
+    "image_id": "wine_000059",
+    "filename": "wine_000059.jpg",
+    "original_filename": "147014h.jpg",
+    "file_size": 57477
+  },
+  {
+    "image_id": "wine_000060",
+    "filename": "wine_000060.jpg",
+    "original_filename": "121312.jpg",
+    "file_size": 196503
+  },
+  {
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