Duplicate from C-three-AN-Nourich/Recipe-1m-processed

Co-authored-by: Rajarshi Roy <Rajarshi-Roy-research@users.noreply.huggingface.co>

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README.md

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+# Recipe1M Processed Dataset Collection
+<!-- 
+[![Dataset](https://img.shields.io/badge/Dataset-Recipe1M-orange?style=flat-square)](https://github.com/yourusername/recipe1m-processed)
+[![License](https://img.shields.io/badge/License-Check%20Original-blue?style=flat-square)](LICENSE.md) -->
+
+## Abstract
+
+This repository provides two complementary processed datasets derived from the Recipe1M dataset:
+
+1. **Instruction-Ingredient Alignment Dataset** (`processed_instructions.json`): Fine-grained instruction-ingredient alignment at the fragment level with multi-granularity ingredient representations
+2. **Multimodal Recipe Dataset** (`train_data.json`, `val_data.json`, `test_data.json`): Recipe data with ingredient-image mappings and recipe images for multimodal learning
+
+Together, these datasets enable comprehensive recipe understanding tasks including ingredient recognition, instruction generation, multimodal learning, and cooking process modeling.
+
+---
+
+## Table of Contents
+
+- [Dataset Overview](#dataset-overview)
+- [Dataset 1: Instruction-Ingredient Alignment](#dataset-1-instruction-ingredient-alignment)
+- [Dataset 2: Multimodal Recipe Data](#dataset-2-multimodal-recipe-data)
+- [Combined Usage Examples](#combined-usage-examples)
+- [Use Cases](#use-cases)
+- [License](#license)
+- [Citation](#citation)
+
+---
+
+## Dataset Overview
+
+| Dataset | Files | Key Features | Primary Use Cases |
+|---------|-------|--------------|-------------------|
+| **Instruction-Ingredient Alignment** | `processed_instructions.json` | Fragment-level alignment, multi-granularity ingredients | Instruction parsing, ingredient extraction, process understanding |
+| **Multimodal Recipe Data** | `train_data.json`, `val_data.json`, `test_data.json` | Image mappings, standardized ingredients, train/val/test splits | Multimodal learning, image-based retrieval, model training |
+
+---
+
+## Dataset 1: Instruction-Ingredient Alignment
+
+### Overview
+
+Fine-grained instruction-ingredient alignment where each cooking instruction is fragmented at ingredient boundaries and aligned with corresponding ingredient information at three levels of granularity.
+
+### File Structure
+
+**processed_instructions.json**
+- **Format**: JSON dictionary with recipe IDs as keys
+- **Recipe ID Format**: 10-character hexadecimal (e.g., `e40cfb8510`)
+
+### Data Schema
+
+```json
+{
+  "id": "recipe_id",
+  "instructions": [
+    {
+      "instruction": "cooking step text fragment",
+      "raw_ing": "original ingredient with quantity",
+      "final_ing": "standardized ingredient name",
+      "no_with_ing": "ingredient without modifiers"
+    }
+  ]
+}
+```
+
+### Field Descriptions
+
+#### Top-Level Fields
+
+| Field | Type | Description | Example |
+|-------|------|-------------|---------|
+| `id` | String | Unique recipe identifier | `"e40cfb8510"` |
+| `instructions` | List | Cooking steps with ingredient alignment | See below |
+
+#### Instruction Object Fields
+
+| Field | Type | Description | Example |
+|-------|------|-------------|---------|
+| `instruction` | String | Instruction text fragment | `"stir together flour,"` |
+| `raw_ing` | String/null | Original ingredient with quantities | `"2 c. flour"` |
+| `final_ing` | String/null | Standardized ingredient name | `"flour"` |
+| `no_with_ing` | String/null | Ingredient without modifiers | `"flour"` |
+
+**Note**: `null` values indicate instruction steps without specific ingredient references (e.g., cooking techniques, equipment preparation).
+
+### Example Data
+
+```json
+{
+  "id": "e40cfb8510",
+  "instructions": [
+    {
+      "instruction": "stir together flour,",
+      "raw_ing": "2 c. flour",
+      "final_ing": "flour",
+      "no_with_ing": "flour"
+    },
+    {
+      "instruction": " soda ",
+      "raw_ing": "1 teaspoon baking soda",
+      "final_ing": "baking soda",
+      "no_with_ing": "baking soda"
+    },
+    {
+      "instruction": "and salt.",
+      "raw_ing": "1/2 teaspoon salt",
+      "final_ing": "salt",
+      "no_with_ing": "salt"
+    },
+    {
+      "instruction": "stir into a very stiff batter.",
+      "raw_ing": null,
+      "final_ing": null,
+      "no_with_ing": null
+    },
+    {
+      "instruction": "bake 1 hour at 325 degrees.",
+      "raw_ing": null,
+      "final_ing": null,
+      "no_with_ing": null
+    }
+  ]
+}
+```
+
+### Key Features
+
+- **Instruction Fragmentation**: Instructions split at ingredient boundaries while maintaining grammatical structure
+- **Multi-Granularity**: Three levels of ingredient representation (raw, final, no modifiers)
+- **Complete Process**: Includes all steps, both ingredient-specific and technique-only
+- **Ingredient Repetition**: Tracks multiple uses of the same ingredient throughout the recipe
+
+---
+
+## Dataset 2: Multimodal Recipe Data
+
+### Overview
+
+Structured recipe data with ingredient-image mappings, standardized ingredient names, and associated recipe images. Split into training, validation, and test sets for machine learning workflows.
+
+### File Structure
+
+| File | Purpose | Recipe IDs Example |
+|------|---------|-------------------|
+| `train_data.json` | Training set | `df203c7b00`, `da3722f92a`, ... |
+| `val_data.json` | Validation set | `da3722f92a`, ... |
+| `test_data.json` | Test set | `da36dcb1f9`, ... |
+
+### Data Schema
+
+```json
+{
+  "recipe_id": {
+    "ing_img_ids": [image_id or null, ...],
+    "ing_text_18k": ["standardized_name" or null, ...],
+    "ing_texts": ["original ingredient text" or null, ...],
+    "instructions": ["step 1", "step 2", ...],
+    "recipe_img_ids": ["image1.jpg", "image2.jpg", ...]
+  }
+}
+```
+
+### Field Descriptions
+
+| Field | Type | Description | Example |
+|-------|------|-------------|---------|
+| `ing_img_ids` | List[int/null] | Image IDs from ingredient database | `[17263, 8861, None, ...]` |
+| `ing_text_18k` | List[str/null] | Standardized names (18K vocabulary) | `['butter', 'olive oil', None, ...]` |
+| `ing_texts` | List[str/null] | Original text with quantities | `['1 teaspoon butter', '2 teaspoons olive oil', ...]` |
+| `instructions` | List[str] | Cooking steps in order | `['melt butter', 'add garlic', ...]` |
+| `recipe_img_ids` | List[str] | Recipe image filenames | `['50cbeb2173.jpg', '95f9aa7309.jpg', ...]` |
+
+### Example Data
+
+```json
+{
+  "da3722f92a": {
+    "ing_img_ids": [17263, 8861, 11498, 3496, None, 578],
+    "ing_text_18k": ["butter", "olive oil", "garlic cloves", "shrimp", None, "lemon"],
+    "ing_texts": [
+      "1 teaspoon butter",
+      "2 teaspoons olive oil",
+      "3 garlic cloves, minced or pressed",
+      "1 lb california shrimp or 1 lb spiny lobster",
+      None,
+      "1 lemon, juice of"
+    ],
+    "instructions": [
+      "melt butter and oil together in saute pan.",
+      "add garlic, saute for one minute, and add shrimp.",
+      "saute for one minute, add wine, lemon juice, salt, and pepper.",
+      "saute quickly while sauce reduces and shrimp turns pink.",
+      "do not overcook.",
+      "sprinkle with parsley before serving."
+    ],
+    "recipe_img_ids": [
+      "50cbeb2173.jpg",
+      "95f9aa7309.jpg",
+      "b81c7c2b13.jpg",
+      "977929a1ff.jpg"
+    ]
+  }
+}
+```
+
+### Key Features
+
+- **Multimodal Alignment**: Links text ingredients to visual representations
+- **Standardization**: 18K ingredient vocabulary for consistency
+- **Image References**: Both ingredient images and recipe images
+- **Train/Val/Test Split**: Ready for supervised learning workflows
+- **Null Handling**: Graceful handling of missing image mappings
+
+
+---
+
+
+---
+
+## Related Resources
+
+These processed datasets build upon:
+- **Recipe1M Dataset**: Original source of recipe data and images
+- **Ingredient Image Database**: Referenced by `ing_img_ids`
+- **Recipe Image Database**: Referenced by `recipe_img_ids`
+- **18K Ingredient Vocabulary**: Used for standardization
+
+---
+
+## License
+
+Please refer to the original Recipe1M dataset license and terms of use. These processed versions maintain the same licensing requirements as the source dataset.
+
+---
+
+## Citation
+
+If you use these datasets, please cite the original Recipe1M work:
+
+```bibtex
+@inproceedings{salvador2017learning,
+  title={Learning Cross-Modal Embeddings for Cooking Recipes and Food Images},
+  author={Salvador, Amaia and Hynes, Nicholas and Aytar, Yusuf and Marin, Javier and 
+          Ofli, Ferda and Weber, Ingmar and Torralba, Antonio},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  year={2017}
+}
+```
+<!-- 
+For these processed datasets:
+
+```bibtex
+@misc{recipe1m_processed_collection,
+  title={Recipe1M Processed Dataset Collection: Instruction-Ingredient Alignment and Multimodal Recipe Data},
+  author={Your Name},
+  year={2025},
+  howpublished={\url{https://github.com/yourusername/recipe1m-processed}},
+}
+``` -->
+
+---
+
+## Acknowledgments
+
+These datasets are derived from the Recipe1M dataset. We acknowledge the original authors for their valuable contribution to the research community. The processing includes instruction-ingredient alignment and multimodal mappings to enable new research directions.
+
+---
+<!-- 
+## Contact
+
+For questions or issues regarding these processed datasets, please open an issue on GitHub or contact [your contact information].
+
+--- -->
+
+## Appendix: Quick Reference
+
+### Dataset 1: Instruction-Ingredient Alignment
+- **Files**: `processed_instructions.json`
+- **Key Features**: Fragment-level alignment, 3 granularity levels
+- **Best For**: Instruction parsing, ingredient extraction
+
+### Dataset 2: Multimodal Recipe Data  
+- **Files**: `train_data.json`, `val_data.json`, `test_data.json`
+- **Key Features**: Image mappings, train/val/test split
+- **Best For**: Multimodal learning, ingredient recognition
+
+### Common Recipe ID Format
+- **Format**: 10-character hexadecimal
+- **Example**: `da3722f92a`, `e40cfb8510`
+- **Usage**: Links recipes across both datasets

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