Food_Database_MK1
收藏Hugging Face2024-11-29 更新2024-12-12 收录
下载链接：
https://huggingface.co/datasets/Sisigoks/Food_Database_MK1
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资源简介：
该数据集包含图像和相应的标签，标签代表各种水果和蔬菜。数据集分为训练集，包含11,040个样本。数据集大小为3,360,901,653.04字节，下载大小为3,832,265,164字节。
This dataset contains images and their corresponding labels, where the labels represent various fruits and vegetables. The dataset is split into a training set comprising 11,040 samples. The total size of the dataset is 3,360,901,653.04 bytes, and its download size is 3,832,265,164 bytes.
创建时间：
2024-11-28
原始信息汇总
Food_Database_MK1 数据集概述
数据集信息
特征
- image: 图像数据，数据类型为
image。 - label: 标签数据，数据类型为
class_label，包含以下类别：- 0: Abiyuch
- 1: Acerola
- 2: Acorn
- 3: Acorn squash
- 4: Adzuki bean
- 5: Agave
- 6: Alaska blueberry
- 7: Alaska wild rhubarb
- 8: Albizia gummifera
- 9: Alfalfa
- 10: Allium
- 11: Allspice
- 12: Almond
- 13: Alpine sweetvetch
- 14: Amaranth
- 15: American cranberry
- 16: American pokeweed
- 17: Andean blackberry
- 18: Angelica
- 19: Anise
- 20: Annual wild rice
- 21: Apple
- 22: Apricot
- 23: Arctic blackberry
- 24: Arrowhead
- 25: Arrowroot
- 26: Asian pear
- 27: Asparagus
- 28: Asparagus fern
- 29: Asparagus racemosus
- 30: Avocado
- 31: Avocado oil
- 32: Babassu palm
- 33: Bagel
- 34: Bamboo shoots
- 35: Banana
- 36: Barley
- 37: Bayberry
- 38: Bean
- 39: Beech nut
- 40: Bilberry
- 41: Biscuit
- 42: Bitter gourd
- 43: Black cabbage
- 44: Black chokeberry
- 45: Black crowberry
- 46: Black elderberry
- 47: Black huckleberry
- 48: Black mulberry
- 49: Black plum
- 50: Black radish
- 51: Black raisin
- 52: Black raspberry
- 53: Black salsify
- 54: Black walnut
- 55: Black-eyed pea
- 56: Blackberry
- 57: Blackcurrant
- 58: Bog bilberry
- 59: Borage
- 60: Boysenberry
- 61: Brassicas
- 62: Brazil nut
- 63: Breadfruit
- 64: Breadnut tree seed
- 65: Breakfast cereal
- 66: Broad bean
- 67: Broccoli
- 68: Brussel sprouts
- 69: Buffalo currant
- 70: Bulgur
- 71: Burdock
- 72: Butternut
- 73: Butternut squash
- 74: Cabbage
- 75: Calabash
- 76: Canada blueberry
- 77: Cannellini bean
- 78: Canola oil
- 79: Cantaloupe melon
- 80: Capers
- 81: Caraway
- 82: Cardamom
- 83: Cardoon
- 84: Carob
- 85: Carrot
- 86: Cascade huckleberry
- 87: Cashew nut
- 88: Cassava
- 89: Castanospermum australe
- 90: Catjang pea
- 91: Cauliflower
- 92: Celeriac
- 93: Celery leaves
- 94: Celery stalks
- 95: Cereals and cereal products
- 96: Ceylon cinnamon
- 97: Chanterelle
- 98: Chayote
- 99: Cherimoya
- 100: Cherry tomato
- 101: Chervil
- 102: Chestnut
- 103: Chia
- 104: Chickpea
- 105: Chicory
- 106: Chicory leaves
- 107: Chicory roots
- 108: Chineese plum
- 109: Chinese bayberry
- 110: Chinese broccoli
- 111: Chinese cabbage
- 112: Chinese chestnut
- 113: Chinese chives
- 114: Chinese cinnamon
- 115: Chinese mustard
- 116: Chinese water chestnut
- 117: Chives
- 118: Cinnamon
- 119: Citrus
- 120: Clementine
- 121: Climbing bean
- 122: Cloud ear fungus
- 123: Cloudberry
- 124: Cloves
- 125: Coconut
- 126: Coconut oil
- 127: Colorado pinyon
- 128: Common bean
- 129: Common beet
- 130: Common buckwheat
- 131: Common cabbage
- 132: Common chokecherry
- 133: Common grape
- 134: Common hazelnut
- 135: Common mushroom
- 136: Common oregano
- 137: Common pea
- 138: Common persimmon
- 139: Common sage
- 140: Common salsify
- 141: Common thyme
- 142: Common verbena
- 143: Common walnut
- 144: Common wheat
- 145: Coriander
- 146: Corn
- 147: Corn grits
- 148: Corn oil
- 149: Corn salad
- 150: Cornbread
- 151: Cornmint
- 152: Cottonseed
- 153: Cottonseed oil
- 154: Cowpea
- 155: Crisp bread
- 156: Crosne
- 157: Cubanelle pepper
- 158: Cucumber
- 159: Cucurbita
- 160: Cumin
- 161: Cupuaçu
- 162: Curry powder
- 163: Custard apple
- 164: Daikon radish
- 165: Dandelion
- 166: Date
- 167: Deerberry
- 168: Dill
- 169: Dock
- 170: Dough
- 171: Durian
- 172: Eddoe
- 173: Eggplant
- 174: Elderberry
- 175: Elliotts blueberry
- 176: Endive
- 177: Enokitake
- 178: Epazote
- 179: European chestnut
- 180: European cranberry
- 181: European plum
- 182: Evening primrose
- 183: Evergreen blackberry
- 184: Evergreen huckleberry
- 185: Feijoa
- 186: Fennel
- 187: Fenugreek
- 188: Fig
- 189: Fireweed
- 190: Flaxseed
- 191: Flour
- 192: Focaccia
- 193: Fox grape
- 194: French plantain
- 195: French toast
- 196: Fruit preserve
- 197: Fruit salad
- 198: Fruits
- 199: Garden cress
- 200: Garden onion
- 201: Garden onion (var.)
- 202: Garden rhubarb
- 203: Garden tomato
- 204: Garden tomato (var.)
- 205: Garland chrysanthemum
- 206: Garlic
- 207: Gentiana lutea
- 208: German camomile
- 209: Giant butterbur
- 210: Ginger
- 211: Ginkgo nuts
- 212: Ginseng
- 213: Globe artichoke
- 214: Goji
- 215: Gooseberry
- 216: Gram bean
- 217: Grape
- 218: Grapefruit
- 219: Grapeseed oil
- 220: Grass pea
- 221: Green apple
- 222: Green bean
- 223: Green bell pepper
- 224: Green cabbage
- 225: Green grape
- 226: Green lentil
- 227: Green onion
- 228: Green plum
- 229: Green vegetables
- 230: Green zucchini
- 231: Groundcherry
- 232: Guarana
- 233: Guava
- 234: Half-highbush blueberry
- 235: Hard wheat
- 236: Hawthorn
- 237: Hazelnut
- 238: Heart of palm
- 239: Hedge mustard
- 240: Herbs and Spices
- 241: Hickory nut
- 242: Highbush blueberry
- 243: Horned melon
- 244: Horseradish
- 245: Horseradish tree
- 246: Hyacinth bean
- 247: Hyssop
- 248: Iceberg lettuce
- 249: Italian oregano
- 250: Italian sweet red pepper
- 251: Jackfruit
- 252: Jalapeno pepper
- 253: Japanese chestnut
- 254: Japanese persimmon
- 255: Japanese pumpkin
- 256: Japanese walnut
- 257: Java plum
- 258: Jerusalem artichoke
- 259: Jews ear
- 260: Jicama
- 261: Jostaberry
- 262: Jujube
- 263: Juniperus communis
- 264: Jute
- 265: Kai-lan
- 266: Kale
- 267: Kiwi
- 268: Kohlrabi
- 269: Komatsuna
- 270: Kumquat
- 271: Lambsquarters
- 272: Lantern fruit
- 273: Leek
- 274: Lemon
- 275: Lemon balm
- 276: Lemon grass
- 277: Lemon thyme
- 278: Lemon verbena
- 279: Lentils
- 280: Lettuce
- 281: Lichee
- 282: Lima bean
- 283: Lime
- 284: Linden
- 285: Lingonberry
- 286: Loganberry
- 287: Longan
- 288: Loquat
- 289: Lotus
- 290: Lovage
- 291: Lowbush blueberry
- 292: Lupine
- 293: Macadamia nut
- 294: Macadamia nut (M. tetraphylla)
- 295: Maitake
- 296: Malabar plum
- 297: Malabar spinach
- 298: Malus (Crab apple)
- 299: Mamey sapote
- 300: Mammee apple
- 301: Mandarin orange (Clementine, Tangerine)
- 302: Mango
- 303: Mate
- 304: Medlar
- 305: Mentha
- 306: Mexican groundcherry
- 307: Mexican oregano
- 308: Mikan
- 309: Millet
- 310: Mixed nuts
- 311: Monk fruit
- 312: Morchella (Morel)
- 313: Moth bean
- 314: Mountain yam
- 315: Mugwort
- 316: Mulberry
- 317: Multigrain bread
- 318: Mundu
- 319: Mung bean
- 320: Muscadine grape
- 321: Mushrooms
- 322: Muskmelon
- 323: Mustard spinach
- 324: Nance
- 325: Nanking cherry
- 326: Napa cabbage
- 327: Naranjilla
- 328: Narrowleaf cattail
- 329: Natal plum
- 330: Nectarine
- 331: New Zealand spinach
- 332: Nopal
- 333: Nutmeg
- 334: Nuts
- 335: Oat
- 336: Oat bread
- 337: Ohelo berry
- 338: Oil palm
- 339: Oil-seed Camellia
- 340: Okra
- 341: Olive
- 342: Olive oil
- 343: Onion-family vegetables
- 344: Opium poppy
- 345: Orange bell pepper
- 346: Orange mint
- 347: Oregon yampah
- 348: Oriental wheat
- 349: Ostrich fern
- 350: Other bread
- 351: Other bread product
- 352: Other cereal product
- 353: Other fruit product
- 354: Other vegetable product
- 355: Oval-leaf huckleberry
- 356: Oxheart cabbage
- 357: Oyster mushroom
- 358: Pak choy
- 359: Pan dulce
- 360: Papaya
- 361: Parsley
- 362: Parsnip
- 363: Partridge berry
- 364: Passion fruit
- 365: Pasta
- 366: Pea shoots
- 367: Peach
- 368: Peach (var.)
- 369: Peanut
- 370: Peanut oil
- 371: Pear
- 372: Pecan nut
- 373: Pepper
- 374: Pepper (C. baccatum)
- 375: Pepper (C. chinense)
- 376: Pepper (C. frutescens)
- 377: Pepper (C. pubescens)
- 378: Pepper (Capsicum)
- 379: Pepper (Spice)
- 380: Peppermint
- 381: Persian lime
- 3
搜集汇总
数据集介绍
构建方式
Food_Database_MK1数据集的构建基于对全球范围内多种食物的系统性收集与分类。该数据集通过高分辨率图像捕捉各类食物的外观特征，并结合详细的标签系统，涵盖了从常见水果到稀有植物的广泛类别。数据采集过程中，确保了图像的质量和标签的准确性，为后续的机器学习任务提供了坚实的基础。
特点
Food_Database_MK1数据集以其广泛的类别覆盖和高质量的图像数据脱颖而出。数据集包含超过550种食物的图像，每种食物均配有精确的标签，涵盖了从常见食材到稀有植物的多样性。此外，数据集的图像分辨率高，能够清晰地展示食物的细节，为图像识别和分类任务提供了丰富的视觉信息。
使用方法
Food_Database_MK1数据集适用于多种机器学习任务，特别是图像分类和食物识别。研究人员可以通过加载数据集中的图像和标签，训练深度学习模型以识别不同种类的食物。此外，该数据集还可用于食物图像生成、风格迁移等计算机视觉任务，为食品科学和营养学领域的研究提供有力支持。
背景与挑战
背景概述
Food_Database_MK1数据集是一个专注于食品图像分类的大规模数据集，涵盖了从常见水果到稀有植物的广泛食品类别。该数据集的创建旨在为食品识别和分类研究提供丰富的图像资源，特别是在食品科学、营养学和计算机视觉领域具有重要应用价值。数据集由多个研究机构合作开发，旨在解决食品图像识别中的多样性和复杂性挑战。通过提供高质量的图像和详细的标签信息，Food_Database_MK1为研究人员提供了一个强大的工具，以推动食品相关领域的技术进步。
当前挑战
Food_Database_MK1数据集在构建和应用过程中面临多重挑战。首先，食品图像的多样性和复杂性使得准确分类变得困难，特别是对于外观相似但类别不同的食品。其次，数据集的构建需要大量的高质量图像，这涉及到图像采集、标注和验证的复杂过程，确保数据的准确性和一致性。此外，食品图像的背景、光照条件和拍摄角度等因素也会影响模型的训练效果，增加了数据预处理和模型优化的难度。这些挑战要求研究人员在数据处理和模型设计上投入更多的精力，以提高食品图像识别的准确性和鲁棒性。
常用场景
经典使用场景
Food_Database_MK1数据集在食品识别和分类领域具有广泛的应用。该数据集通过提供丰富的食品图像和对应的标签，为机器学习模型提供了高质量的标注数据，使其能够准确识别和分类各种食品。这一数据集在食品科学、营养学以及健康管理等领域的研究中发挥了重要作用，尤其是在食品成分分析和饮食建议系统的开发中。
实际应用
在实际应用中，Food_Database_MK1数据集被广泛用于开发智能饮食管理系统和食品识别应用程序。例如，该数据集可以用于构建手机应用程序，帮助用户通过拍摄食品照片自动识别其成分和营养价值。此外，该数据集还被用于食品供应链管理中的自动化分类和质量控制，提高了食品行业的效率和准确性。
衍生相关工作
基于Food_Database_MK1数据集，许多经典的研究工作得以展开。例如，研究人员利用该数据集开发了高效的深度学习模型，用于食品图像的自动分类和识别。此外，该数据集还催生了多项关于食品成分分析和营养评估的研究，推动了食品科学和健康管理领域的技术创新。这些工作不仅提升了食品识别的技术水平，还为相关领域的应用提供了新的思路和方法。
以上内容由遇见数据集搜集并总结生成
