Can you please add the Stanford dog dataset?
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['would you like to give it a try, @dgrnd4? (maybe with the help of the dataset author?)'
 '@julien-c i am sorry but I have no idea about how it works: can I add the dataset by myself, following "instructions to add a new dataset"?\r\nCan I add a dataset even if it\'s not mine? (it\'s public in the link that I wrote on the post)\r\n'
 "Hi! The [ADD NEW DATASET](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) instructions are indeed the best place to start. It's also perfectly fine to add a dataset if it's public, even if it's not yours. Let me know if you need some additional pointers."
 'If no one is working on this, I could take this up!'
 '@khushmeeet this is the [link](https://huggingface.co/datasets/dgrnd4/stanford_dog_dataset) where I added the dataset already. If you can I would ask you to do this:\r\n1) The dataset it\'s all in TRAINING SET: can you please divide it in Training,Test and Validation Set? If you can for each class, take the 80% for the Training set and the 10% for Test and 10% Validation\r\n2) The images has different size, can you please resize all the images in 224,224,3? Look even at the last dimension "3" because some images has dimension 4!\r\n\r\nThank you!!'
 "Hi @khushmeeet! Thanks for the interest. You can self-assign the issue by commenting `#self-assign` on it. \r\n\r\nAlso, I think we can skip @dgrnd4's steps as we try to avoid any custom processing on top of raw data. One can later copy the script and override `_post_process` in it to perform such processing on the generated dataset."
 "Thanks @mariosasko \r\n\r\n@dgrnd4 As dataset is there on Hub, and preprocessing is not recommended. I am not sure if there is any other task to do. However, I can't seem to find relevant `.py` files for this dataset in GitHub repo."
 '@khushmeeet @mariosasko The point is that the images must be processed and must have the same size in order to can be used for things for example "Training". '
 '@dgrnd4 Yes, but this can be done after loading (`map` to resize images and `train_test_split` to create extra splits)\r\n\r\n@khushmeeet The linked version is implemented as a no-code dataset and is generated directly from the ZIP archive, but our "GitHub" datasets (these are datasets without a user/org namespace on the Hub) need a generation script, and you can find one [here](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/image_classification/stanford_dogs.py). `datasets` started as a fork of TFDS, so we share similar script structure, which makes it trivial to adapt it.'
 '@mariosasko The point is that if I use something like this:\r\nx_train, x_test = train_test_split(dataset, test_size=0.1) \r\n\r\nto get Train 90% and Test 10%, and then to get the Validation Set (10% of the whole 100%):\r\n\r\n```\r\ntrain_ratio = 0.80\r\nvalidation_ratio = 0.10\r\ntest_ratio = 0.10\r\n\r\nx_train, x_test, y_train, y_test = train_test_split(dataX, dataY, test_size=1 - train_ratio)\r\nx_val, x_test, y_val, y_test = train_test_split(x_test, y_test, test_size=test_ratio/(test_ratio + validation_ratio)) \r\n\r\n```\r\n\r\nThe point is that the structure of the data is:\r\n```\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: [\'image\', \'label\'],\r\n        num_rows: 20580\r\n    })\r\n})\r\n\r\n```\r\n\r\nSo how to extract images and labels?\r\n\r\nEDIT --> Split of the dataset in Train-Test-Validation:\r\n```\r\nimport datasets\r\nfrom datasets.dataset_dict import DatasetDict\r\nfrom datasets import Dataset\r\n\r\npercentage_divison_test = int(len(dataset[\'train\'])/100 *10)       # 10%  --> 2058 \r\npercentage_divison_validation = int(len(dataset[\'train\'])/100 *20) # 20%  --> 4116\r\n\r\ndataset_ = datasets.DatasetDict({"train": Dataset.from_dict({\r\n\r\n                                  \'image\':  dataset[\'train\'][0 : len(dataset[\'train\']) ][\'image\'],    \r\n                                  \'labels\': dataset[\'train\'][0 : len(dataset[\'train\']) ][\'label\'] }), \r\n                                 \r\n                                 "test": Dataset.from_dict({  #20580-4116 (validation) ,20580-2058 (test)\r\n                                  \'image\':  dataset[\'train\'][len(dataset[\'train\']) - percentage_divison_validation : len(dataset[\'train\']) - percentage_divison_test][\'image\'], \r\n                                  \'labels\': dataset[\'train\'][len(dataset[\'train\']) - percentage_divison_validation : len(dataset[\'train\']) - percentage_divison_test][\'label\'] }), \r\n                                 \r\n                                  "validation": Dataset.from_dict({ # 20580-2058 (test)\r\n                                  \'image\':  dataset[\'train\'][len(dataset[\'train\']) - percentage_divison_test : len(dataset[\'train\'])][\'image\'], \r\n                                  \'labels\': dataset[\'train\'][len(dataset[\'train\']) - percentage_divison_test : len(dataset[\'train\'])][\'label\'] }), \r\n                                })\r\n```'
 '@mariosasko in order to resize images I\'m trying this method: \r\n```\r\nfor i in range(0,len(dataset[\'train\'])): #len(dataset[\'train\'])\r\n\r\n  ex = dataset[\'train\'][i] #i\r\n  image = ex[\'image\']\r\n  image = image.convert("RGB") # <class \'PIL.Image.Image\'> <PIL.Image.Image image mode=RGB size=500x333 at 0x7F84F1948150>\r\n  image_resized = image.resize(size_to_resize) # <PIL.Image.Image image mode=RGB size=224x224 at 0x7F84F17885D0>\r\n\r\n  dataset[\'train\'][i][\'image\'] = image_resized \r\n```\r\n\r\nBecause the DatasetDict is formed by arrows that are immutable, the changing assignment in the last line of code, doesn\'t work!\r\nDo you have any idea in order to get a valid result?'
 '#self-assign'
 'I have raised PR for adding stanford-dog dataset. I have not added any data preprocessing code. Only dataset generation script is there. Let me know any changes required, or anything to add to README.']
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/ImageNetDogs/)*
- **Motivation:** *The dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It is useful for fine-grain purpose *


Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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