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Datasets:
ibrahimdaud
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multi-label-food-recognition

Tasks:
Image Classification
Modalities:
Image
Text
Formats:
parquet
Size:
10K - 100K
Tags:
food-recognition
multi-label
computer-vision
food-classification
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Auto-converted to Parquet Duplicate
image
imagewidth (px)
512
512
labels
listlengths
2
5
label_names
listlengths
2
5
num_labels
int32
2
5
[ 4, 42, 47, 85 ]
[ "vanilla_yoghurt", "natural_low_fat_yogurt", "oatghurt", "avocado" ]
4
[ 19, 75, 79 ]
[ "coconut", "spiny_gourd", "strawberry_yogurt" ]
3
[ 15, 21, 58, 88 ]
[ "yoghurt", "pineapple", "cucumber", "cantaloupe" ]
4
[ 48, 61, 77, 86 ]
[ "vanilla_yogurt", "potato", "okra", "strawberry" ]
4
[ 0, 15 ]
[ "cantaloupe", "amaranth" ]
2
[ 26, 71, 76, 78 ]
[ "ginger", "soy_milk", "strawberry_yoghurt", "sponge_gourd" ]
4
[ 8, 35, 76 ]
[ "bitter_gourd", "sponge_gourd", "lime" ]
3
[ 38, 68 ]
[ "satsuma", "melon" ]
2
[ 2, 15, 17, 37 ]
[ "apple_juice", "cauliflower", "cantaloupe", "mango" ]
4
[ 15, 32 ]
[ "cantaloupe", "leek" ]
2
[ 10, 70 ]
[ "sour_milk", "blueberry_soy_yogurt" ]
2
[ 37, 56 ]
[ "mango", "pear" ]
2
[ 5, 80, 85 ]
[ "banana", "vanilla_yoghurt", "sweet_potato" ]
3
[ 3, 9, 34, 86 ]
[ "asparagus", "blueberry", "vanilla_yogurt", "lettuce" ]
4
[ 76, 79 ]
[ "sponge_gourd", "strawberry_yogurt" ]
2
[ 12, 66, 79 ]
[ "bottle_gourd", "red_grapefruit_juice", "strawberry_yogurt" ]
3
[ 69, 85 ]
[ "sour_cream", "vanilla_yoghurt" ]
2
[ 10, 38 ]
[ "melon", "blueberry_soy_yogurt" ]
2
[ 25, 55 ]
[ "peach", "garlic" ]
2
[ 6, 41, 70, 73, 86 ]
[ "sour_milk", "mushroom", "soyghurt", "vanilla_yogurt", "beetroot" ]
5
[ 5, 48, 76 ]
[ "okra", "banana", "sponge_gourd" ]
3
[ 6, 27, 37, 50, 74 ]
[ "mango", "spinach", "beetroot", "golden_grapefruit_juice", "orange" ]
5
[ 47, 51 ]
[ "oatghurt", "orange_juice" ]
2
[ 8, 53, 76 ]
[ "bitter_gourd", "sponge_gourd", "papaya" ]
3
[ 24, 61 ]
[ "potato", "fig" ]
2
[ 16, 71 ]
[ "carrot", "soy_milk" ]
2
[ 51, 73 ]
[ "soyghurt", "orange_juice" ]
2
[ 24, 41, 60, 62 ]
[ "mushroom", "pumpkin", "fig", "pomegranate" ]
4
[ 43, 47, 84 ]
[ "natural_yogurt", "vanilla_soyghurt", "oatghurt" ]
3
[ 36, 61, 88 ]
[ "mandarin_juice", "yoghurt", "potato" ]
3
[ 41, 65, 78 ]
[ "mushroom", "red_grapefruit", "strawberry_yoghurt" ]
3
[ 19, 74 ]
[ "spinach", "coconut" ]
2
[ 0, 6, 27, 89 ]
[ "beetroot", "golden_grapefruit_juice", "amaranth", "zucchini" ]
4
[ 46, 68 ]
[ "satsuma", "oat_yogurt" ]
2
[ 3, 41, 57, 72, 87 ]
[ "watermelon", "peas", "asparagus", "soybean", "mushroom" ]
5
[ 46, 59, 63 ]
[ "plum", "radish", "oat_yogurt" ]
3
[ 34, 54, 59, 78 ]
[ "lettuce", "plum", "passion_fruit", "strawberry_yoghurt" ]
4
[ 24, 50, 54 ]
[ "passion_fruit", "fig", "orange" ]
3
[ 12, 27, 36, 74 ]
[ "mandarin_juice", "spinach", "bottle_gourd", "golden_grapefruit_juice" ]
4
[ 5, 30, 51 ]
[ "orange_juice", "jalapeno", "banana" ]
3
[ 16, 68, 81 ]
[ "carrot", "tomato", "satsuma" ]
3
[ 25, 59, 77 ]
[ "garlic", "strawberry", "plum" ]
3
[ 8, 32 ]
[ "leek", "bitter_gourd" ]
2
[ 11, 25, 27, 37 ]
[ "mango", "blueberry_soyghurt", "golden_grapefruit_juice", "garlic" ]
4
[ 11, 54 ]
[ "blueberry_soyghurt", "passion_fruit" ]
2
[ 10, 34, 52 ]
[ "orange_red_grapefruit_juice", "lettuce", "blueberry_soy_yogurt" ]
3
[ 59, 69 ]
[ "sour_cream", "plum" ]
2
[ 2, 46 ]
[ "apple_juice", "oat_yogurt" ]
2
[ 2, 15, 18, 24, 28 ]
[ "apple_juice", "grapefruit_juice", "fig", "cantaloupe", "chili_pepper" ]
5
[ 21, 28, 71 ]
[ "grapefruit_juice", "soy_milk", "cucumber" ]
3
[ 54, 78 ]
[ "strawberry_yoghurt", "passion_fruit" ]
2
[ 28, 41, 77 ]
[ "grapefruit_juice", "strawberry", "mushroom" ]
3
[ 2, 80 ]
[ "apple_juice", "sweet_potato" ]
2
[ 9, 42, 79 ]
[ "natural_low_fat_yogurt", "strawberry_yogurt", "blueberry" ]
3
[ 5, 48, 68 ]
[ "satsuma", "banana", "okra" ]
3
[ 21, 56, 65 ]
[ "red_grapefruit", "cucumber", "pear" ]
3
[ 9, 13 ]
[ "blueberry", "broccoli" ]
2
[ 24, 73 ]
[ "fig", "soyghurt" ]
2
[ 30, 48, 70, 71 ]
[ "okra", "sour_milk", "jalapeno", "soy_milk" ]
4
[ 55, 78 ]
[ "peach", "strawberry_yoghurt" ]
2
[ 47, 71 ]
[ "soy_milk", "oatghurt" ]
2
[ 8, 24, 47, 89 ]
[ "fig", "bitter_gourd", "zucchini", "oatghurt" ]
4
[ 0, 28, 75, 79 ]
[ "strawberry_yogurt", "spiny_gourd", "amaranth", "grapefruit_juice" ]
4
[ 44, 52 ]
[ "nectarine", "orange_red_grapefruit_juice" ]
2
[ 32, 66 ]
[ "leek", "red_grapefruit_juice" ]
2
[ 19, 54 ]
[ "coconut", "passion_fruit" ]
2
[ 20, 46, 47, 87 ]
[ "watermelon", "oat_yogurt", "oatghurt", "corn" ]
4
[ 9, 67 ]
[ "blueberry", "ridge_gourd" ]
2
[ 50, 65, 81, 89 ]
[ "zucchini", "red_grapefruit", "tomato", "orange" ]
4
[ 26, 29, 52 ]
[ "orange_red_grapefruit_juice", "grapes", "ginger" ]
3
[ 20, 46, 51, 75 ]
[ "orange_juice", "corn", "spiny_gourd", "oat_yogurt" ]
4
[ 0, 3 ]
[ "amaranth", "asparagus" ]
2
[ 20, 76, 83 ]
[ "sponge_gourd", "corn", "vanilla_soy_yogurt" ]
3
[ 0, 9 ]
[ "amaranth", "blueberry" ]
2
[ 47, 76, 89 ]
[ "oatghurt", "sponge_gourd", "zucchini" ]
3
[ 2, 84 ]
[ "vanilla_soyghurt", "apple_juice" ]
2
[ 24, 25 ]
[ "fig", "garlic" ]
2
[ 15, 83 ]
[ "cantaloupe", "vanilla_soy_yogurt" ]
2
[ 20, 59 ]
[ "corn", "plum" ]
2
[ 34, 76 ]
[ "sponge_gourd", "lettuce" ]
2
[ 47, 49, 63 ]
[ "radish", "onion", "oatghurt" ]
3
[ 47, 86, 89 ]
[ "oatghurt", "vanilla_yogurt", "zucchini" ]
3
[ 23, 53 ]
[ "papaya", "eggplant" ]
2
[ 3, 70, 71 ]
[ "soy_milk", "sour_milk", "asparagus" ]
3
[ 52, 68, 83 ]
[ "satsuma", "vanilla_soy_yogurt", "orange_red_grapefruit_juice" ]
3
[ 46, 84 ]
[ "vanilla_soyghurt", "oat_yogurt" ]
2
[ 0, 35, 42 ]
[ "amaranth", "lime", "natural_low_fat_yogurt" ]
3
[ 13, 16, 41 ]
[ "mushroom", "broccoli", "carrot" ]
3
[ 48, 62, 79 ]
[ "pumpkin", "okra", "strawberry_yogurt" ]
3
[ 13, 65 ]
[ "broccoli", "red_grapefruit" ]
2
[ 47, 70 ]
[ "sour_milk", "oatghurt" ]
2
[ 11, 24, 58, 63 ]
[ "radish", "blueberry_soyghurt", "pineapple", "fig" ]
4
[ 58, 79 ]
[ "strawberry_yogurt", "pineapple" ]
2
[ 15, 27, 52, 79 ]
[ "strawberry_yogurt", "golden_grapefruit_juice", "orange_red_grapefruit_juice", "cantaloupe" ]
4
[ 47, 48 ]
[ "okra", "oatghurt" ]
2
[ 22, 40, 51 ]
[ "dragon_fruit", "mixed_juice", "orange_juice" ]
3
[ 32, 35, 84, 85 ]
[ "leek", "vanilla_yoghurt", "lime", "vanilla_soyghurt" ]
4
[ 34, 37, 57, 84 ]
[ "peas", "vanilla_soyghurt", "mango", "lettuce" ]
4
[ 15, 28 ]
[ "grapefruit_juice", "cantaloupe" ]
2
[ 12, 75, 82, 88 ]
[ "turnip", "yoghurt", "spiny_gourd", "bottle_gourd" ]
4
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YAML Metadata Warning: The task_categories "multi-label-classification" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Multi-Label Food Recognition Dataset

This is a multi-label food recognition dataset generated from single-class food images. Each image contains 2-5 different food items composited together using natural composition methods.

Dataset Details

  • Total Images: 13,000
  • Training Images: 10,400 (80%)
  • Validation Images: 2,600 (20%)
  • Number of Classes: 90
  • Labels per Image: 2-5 labels
  • Image Format: RGB, 512x512 pixels
  • File Format: Parquet

Dataset Structure

Each sample contains:

  • image: PIL Image (RGB, 512x512)
  • labels: List of integer label IDs (multi-hot encoded)
  • label_names: List of string class names
  • num_labels: Number of labels in the image (2-5)

Usage 

from datasets import load_dataset

# Load dataset
dataset = load_dataset("ibrahimdaud/multi-label-food-recognition")

# Access splits
train_data = dataset['train']
val_data = dataset['validation']

# Example: Get first training sample
sample = train_data[0]
print(f"Image: {sample['image']}")
print(f"Labels: {sample['label_names']}")
print(f"Label IDs: {sample['labels']}")

Citation

If you use this dataset, please cite:

@dataset{multi_label_food_recognition,
  title={Multi-Label Food Recognition Dataset},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/datasets/ibrahimdaud/multi-label-food-recognition}
}

License

MIT License

Downloads last month
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Size of downloaded dataset files:
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Size of the auto-converted Parquet files:
3.1 GB
Number of rows:
13,000

Models trained or fine-tuned on ibrahimdaud/multi-label-food-recognition