Preprocessed datasets¶
In H2O Hydrogen Torch, you can access preprocessed datasets to explore supported problem types.
Import preprocessed dataset¶
To import a preprocessed dataset to H2O Hydrogen Torch, consider the following instructions:
- In the H2O Hydrogen Torch navigation menu, click Import dataset.
- In the S3 file name list, select select one of the Preprocessed Datasets in H2O Hydrogen Torch.
- Click Continue.
- Again, click Continue.
Note
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After importing a preprocessed dataset, you will be able to use it for an experiment.
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To learn how to preprocess your dataset for a particular supported problem type, see Dataset Formats
Preprocessed datasets in H2O Hydrogen Torch¶
Flower image classification¶
File name: flower_image_classification.zip
Description: The dataset contains images of dandelions, daisies, roses, tulips, and sunflowers.
To learn more about the dataset, see Flowers Dataset.
Dataset Columns: image
, label
Problem Type: Image classification
Coins image regression¶
File name: coins_image_regression.zip
Description: The dataset contains a collection of images with one or more coins. Each image has been labeled to indicate the sum of its coins. The currency of the coins is the Brazilian Real (R$).
To learn more about the dataset, see Brazilian Coins.
Dataset Columns: image_path
, label
, fold
Problem Type: Image regression
Global wheat image object detection¶
File name: globalwheat_image_object_detection.zip
Description: The dataset contains a collection of images of wheat fields with bounding boxes for each identified wheat head.
To learn more about the dataset, see Global Wheat Dataset.
Dataset Columns: image
, class_id
, x_min
, y_min
, x_max
, y_max
Problem Type: Single-class object detection
Amazon Review text classification¶
File name: amazon_reviews_text_classification.csv
Description: The dataset contains a collection of reviews from Amazon. Each review (in text form) includes the title of the review and the review itself. The dataset has been labeled to indicate whether a review is positive or negative.
To learn more about the dataset, see Amazon product data.
Dataset Columns: text
, label
Problem Type: Text classification
Stanford bicycle image metric learning¶
File name: bicycle_image_metric_learning.zip
Description: The dataset contains images of online product ads for bicycles. Each ad has multiple images marked by their class ID.
To learn more about the dataset, see The Stanford Online Products dataset.
Dataset Columns: image
, label
, fold
Problem Type: Image metric learning
Fashion image semantic segmentation¶
File name: fashion_image_semantic_segmentation.zip
Description: The dataset contains images corresponding to fashion/apparel segmentations. This dataset contains images of people wearing various clothing types in multiple poses.
To learn more about the dataset, see Clothing Co-Parsing Dataset.
Dataset Columns: image
, class_id
, rle_mask
Problem Type: Semantic segmentation
CNN/Daily mail text sequence to sequence¶
File name: cnn_dailymail_text_sequence_to_sequence.zip
Description: The dataset contains human-generated abstract summaries from news stories published on the CNN and Daily Mail websites.
To learn more about the dataset, see abisee/cnn-dailymail.
Dataset Columns: text
, summary
, id
Problem Type: Text sequence to sequence
Well-formed query text regression¶
File name: wellformed_query_text_regression.csv
Description: The dataset contains a collection of search queries. Every query was rated between 0 and 1 specifying whether or not the query was well-formed.
To learn more about the dataset, see Query-wellformedness Dataset.
Dataset Columns: text
, rating
Problem Type: Text regression
CoNLL-2003 text token classification¶
File name: conll2003_text_token_classification.zip
Description: The dataset contains a collection of text pieces that have their name entities specified. Name entities refer to abstract or physical objects such as a person, product, etc., that can be indicated with a proper name.
To learn more about the dataset, see Language-Independent Named Entity Recognition (II).
Dataset Columns: id
, text
, pos_tags
, chunk_tags
, ner_tags
Problem Type: Text token classification
Squad text span prediction¶
File name: squad_text_span_prediction.zip
Description: The dataset contains questions with answers and contexts that can be used to answer the questions.
To learn more about the dataset, see The Stanford Question Answering Dataset.
Dataset Columns: question
, context
, answer
Problem Type: Text span prediction
Ubuntu text metric learning¶
File name: ubuntu_text_metric_learning.zip
Description: The dataset contains a preprocessed collection of questions from AskUbuntu.com. Questions are grouped in similar clusters (label).
To learn more about the dataset and its use in research, refer to the following arXiv paper: Semi-supervised Question Retrieval with Gated Convolutions, NAACL 2016, Tao Lei et al.
To view the original dataset from the authors, visit the following Github repository: AskUbuntu Question Dataset.
Dataset Columns: text
, label
, fold
Problem Type: Text metric learning
COCO cars image instance segmentation¶
File name: coco_image_instance_segmentation.zip
Description: The dataset contains a subsample of the famous Common Objects in Context (COCO) dataset. This subsample includes only a single "Car" class. In other words, all images contain a car or multiple cars.
To learn more about the dataset, see COCO Dataset.
Dataset Columns: image_id
, class_id
, rle_mask
Problem Type: Image instance segmentation
Environmental sound audio classification¶
File name: esc10_audio_classification.zip
Description: The dataset contains 5-second-long recordings organized into ten classes (with 40 examples per class). Clips in this dataset have been manually extracted from public field recordings gathered by the Freesound.org project.
To learn more about the dataset, see ESC-50: Dataset for Environmental Sound Classification.
Dataset Columns: filename
, fold
, label
Problem Type: Audio classification
MNIST audio regression¶
File name: amnist_audio_regression.zip
Description: The dataset contains a collection of 30,000 audio samples of spoken digits (0-9) of sixty different speakers.
To learn more about the dataset, see Audio MNIST.
Dataset Columns: audio
, label
, fold
Problem Type: Audio regression
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