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Datasets:
ethz
/
food101

Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
100K - 1M
Libraries:
Datasets
Dask
License:
Dataset card Viewer Files Community
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albertvillanova HF staff commited on
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1ac27ab
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Convert dataset to Parquet

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Convert dataset to Parquet.

Files changed (13) hide show
  1. README.md +11 -4
  2. data/train-00000-of-00008.parquet +3 -0
  3. data/train-00001-of-00008.parquet +3 -0
  4. data/train-00002-of-00008.parquet +3 -0
  5. data/train-00003-of-00008.parquet +3 -0
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  9. data/train-00007-of-00008.parquet +3 -0
  10. data/validation-00000-of-00003.parquet +3 -0
  11. data/validation-00001-of-00003.parquet +3 -0
  12. data/validation-00002-of-00003.parquet +3 -0
  13. dataset_infos.json +155 -1
README.md CHANGED
@@ -130,13 +130,20 @@ dataset_info:
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  '100': waffles
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  splits:
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  - name: train
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- num_bytes: 3845865322
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  num_examples: 75750
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  - name: validation
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- num_bytes: 1276249954
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  num_examples: 25250
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- download_size: 4998236572
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- dataset_size: 5122115276
 
 
 
 
 
 
 
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  ---
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  # Dataset Card for Food-101
 
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  '100': waffles
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  splits:
132
  - name: train
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+ num_bytes: 3842657187.0
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  num_examples: 75750
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  - name: validation
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+ num_bytes: 1275182340.5
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  num_examples: 25250
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+ download_size: 5059972308
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+ dataset_size: 5117839527.5
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/train-*
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+ - split: validation
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+ path: data/validation-*
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  ---
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  # Dataset Card for Food-101
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dataset_infos.json CHANGED
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- {"default": {"description": "This dataset consists of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.", "citation": " @inproceedings{bossard14,\n title = {Food-101 -- Mining Discriminative Components with Random Forests},\n author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},\n booktitle = {European Conference on Computer Vision},\n year = {2014}\n}\n", "homepage": "https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/", "license": "LICENSE AGREEMENT\n=================\n - The Food-101 data set consists of images from Foodspotting [1] which are not\n property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond\n scientific fair use must be negociated with the respective picture owners\n according to the Foodspotting terms of use [2].\n\n[1] http://www.foodspotting.com/\n[2] http://www.foodspotting.com/terms/\n", "features": {"image": {"id": null, "_type": "Image"}, "label": {"num_classes": 101, "names": ["apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "image", "output": "label"}, "task_templates": [{"task": "image-classification", "image_column": "image", "label_column": "label", "labels": ["apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheese_plate", "cheesecake", "chicken_curry", "chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles"]}], "builder_name": "food101", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3845865322, "num_examples": 75750, "dataset_name": "food101"}, "validation": {"name": "validation", "num_bytes": 1276249954, "num_examples": 25250, "dataset_name": "food101"}}, "download_checksums": {"http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz": {"num_bytes": 4996278331, "checksum": "d97d15e438b7f4498f96086a4f7e2fa42a32f2712e87d3295441b2b6314053a4"}, "https://s3.amazonaws.com/datasets.huggingface.co/food101/meta/train.txt": {"num_bytes": 1468812, "checksum": "2920f7d55473974492b41a01241ccfd71df1b74d29d27b617337f840f58f77ab"}, "https://s3.amazonaws.com/datasets.huggingface.co/food101/meta/test.txt": {"num_bytes": 489429, "checksum": "440d53374697d019a972fe66e8e44031ae80267a126ecb814ad537ec1fd506db"}}, "download_size": 4998236572, "post_processing_size": null, "dataset_size": 5122115276, "size_in_bytes": 10120351848}}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "default": {
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+ "description": "This dataset consists of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.",
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+ "citation": " @inproceedings{bossard14,\n title = {Food-101 -- Mining Discriminative Components with Random Forests},\n author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},\n booktitle = {European Conference on Computer Vision},\n year = {2014}\n}\n",
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+ "homepage": "https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/",
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+ "license": "LICENSE AGREEMENT\n=================\n - The Food-101 data set consists of images from Foodspotting [1] which are not\n property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond\n scientific fair use must be negociated with the respective picture owners\n according to the Foodspotting terms of use [2].\n\n[1] http://www.foodspotting.com/\n[2] http://www.foodspotting.com/terms/\n",
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+ "apple_pie",
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+ "baby_back_ribs",
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+ "baklava",
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+ "beef_carpaccio",
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+ "beef_tartare",
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+ "beet_salad",
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+ "scallops",
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+ "spaghetti_bolognese",
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