Duplicate from SohlHealth/sohl-multidish-yolo-dataset

Co-authored-by: Prajwala Shambulingappa <SohlHealth@users.noreply.huggingface.co>

.gitattributes

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+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.JPG filter=lfs diff=lfs merge=lfs -text
+*.JPEG filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: SOHL Multi-Dish Indian Food Detection Dataset
+emoji: 🍽️
+colorFrom: orange
+colorTo: red
+sdk: static
+pinned: false
+tags:
+- computer-vision
+- object-detection
+- yolo
+- food-detection
+- indian-cuisine
+- multi-dish
+- yolov8
+license: mit
+---
+
+# 🍽️ SOHL Multi-Dish Indian Food Detection Dataset
+
+## Overview
+This dataset contains **377 annotated images** of Indian food plates with **multiple dishes per image**. Designed for training YOLO models to detect and classify multiple food items on a single plate.
+
+## Dataset Statistics
+- **Images**: 377
+- **Annotations**: 377  
+- **Classes**: 16
+- **Format**: YOLOv8 (images + txt annotations)
+- **Created**: 2025-08-16
+
+## Classes
+0. **bread_or_Roti_naan** - Chapati, naan, roti, paratha, and other Indian breads
+1. **curry_dish** - General curry preparations, gravies, and liquid dishes
+2. **rice_dish** - Plain rice, biryani, pulao, and rice preparations
+3. **dry_vegetable** - Bhindi, aloo, cauliflower, and dry sabzi preparations
+4. **snack_item** - Samosa, pakora, vada, dhokla, and fried snacks
+5. **sweet_item** - Traditional sweets, desserts, and mithai
+6. **accompaniment** - Pickle, raita, papad, chutney, and side dishes
+7. **Dal_or_sambar** - Dal preparations, sambar, and lentil-based dishes
+8. **drink** - Beverages, juices, lassi, and liquid refreshments
+9. **eggs** - Egg preparations, omelettes, and egg-based dishes
+10. **fish_dish** - Fish curry, fried fish, and seafood preparations
+11. **fruits** - Fresh fruits, fruit salads, and fruit-based items
+12. **pasta** - Pasta dishes and Italian preparations
+13. **salad** - Vegetable salads, mixed salads, and fresh preparations
+14. **soup** - Soups, broths, and liquid appetizers
+15. **south_indian_breakfast** - Dosa, idli, upma, and South Indian breakfast items
+
+## Dataset Structure
+```
+sohl-multidish-yolo-dataset/
+├── images/           # 377 image files
+├── labels/           # 377 YOLO format annotations
+├── dataset.yaml      # YOLOv8 configuration
+└── README.md         # This file
+```
+
+## Usage
+
+### Download Dataset
+```python
+from huggingface_hub import snapshot_download
+
+# Download entire dataset
+dataset_path = snapshot_download(
+    repo_id="SohlHealth/sohl-multidish-yolo-dataset",
+    repo_type="dataset"
+)
+```
+
+### Train YOLOv8
+```python
+from ultralytics import YOLO
+
+# Load model and train
+model = YOLO('yolov8s.pt')
+results = model.train(
+    data='dataset.yaml',
+    epochs=100,
+    batch=8,
+    imgsz=640
+)
+```
+
+## Key Features
+- ✅ **Multi-dish detection**: 2-6 items per plate
+- ✅ **Indian cuisine focus**: Traditional dishes and combinations  
+- ✅ **Real-world scenarios**: Restaurant and home environments
+- ✅ **Complex layouts**: Overlapping items, various plate styles
+- ✅ **High-quality annotations**: Precise bounding boxes
+- ✅ **Comprehensive classes**: 16 food categories including regional specialties
+
+## Performance Expectations
+Based on similar datasets and architectures:
+- **Expected mAP@0.5**: 15-25% (multi-dish detection is challenging)
+- **Training time**: 3-6 hours on modern GPU
+- **Recommended epochs**: 100-150
+- **Best practices**: Transfer learning from food detection models
+
+## Citation
+```
+@dataset{sohl_multidish_dataset_20250816_161951,
+  title={SOHL Multi-Dish Indian Food Detection Dataset},
+  author={SOHL AI Team},
+  year={2025},
+  url={https://huggingface.co/datasets/SohlHealth/sohl-multidish-yolo-dataset}
+}
+```
+
+## License
+MIT License - See LICENSE file for details.
+
+## Contact
+For questions about this dataset, please contact the SOHL AI team.

dataset.yaml

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+names:
+  0: bread_or_Roti_naan
+  1: curry_dish
+  2: rice_dish
+  3: dry_vegetable
+  4: snack_item
+  5: sweet_item
+  6: accompaniment
+  7: Dal_or_sambar
+  8: drink
+  9: eggs
+  10: fish_dish
+  11: fruits
+  12: pasta
+  13: salad
+  14: soup
+  15: south_indian_breakfast
+path: .
+test: images
+train: images
+val: images

dataset_info.json

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+{
+  "name": "SOHL Multi-Dish Indian Food Detection Dataset",
+  "description": "YOLOv8 dataset for detecting multiple Indian food items on plates",
+  "version": "1.0.0",
+  "created": "2025-08-16T16:19:51.524231",
+  "statistics": {
+    "total_images": 377,
+    "total_labels": 377,
+    "image_formats": [
+      ".jpg"
+    ],
+    "missing_labels": []
+  },
+  "classes": {
+    "0": "bread_or_Roti_naan",
+    "1": "curry_dish",
+    "2": "rice_dish",
+    "3": "dry_vegetable",
+    "4": "snack_item",
+    "5": "sweet_item",
+    "6": "accompaniment",
+    "7": "Dal_or_sambar",
+    "8": "drink",
+    "9": "eggs",
+    "10": "fish_dish",
+    "11": "fruits",
+    "12": "pasta",
+    "13": "salad",
+    "14": "soup",
+    "15": "south_indian_breakfast"
+  },
+  "format": "YOLOv8",
+  "usage": "object_detection",
+  "domain": "food_detection",
+  "cuisine": "indian",
+  "features": [
+    "multi_dish_detection",
+    "complex_layouts",
+    "real_world_scenarios",
+    "high_quality_annotations",
+    "comprehensive_classes"
+  ],
+  "recommended_training": {
+    "epochs": 100,
+    "batch_size": 8,
+    "image_size": 640,
+    "augmentation": "moderate",
+    "transfer_learning": "recommended"
+  }
+}