Let's load MNIST, the hello world dataset of machine learning.
First, instantiate a Dataset by pointing it to the dataset's locations. Datasets hosted on Activeloop Platform are typically identified by the namespace of the organization followed by the dataset name: activeloop/mnist-train.
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import hub
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dataset_path ='hub://activeloop/mnist-train'
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ds = hub.load(dataset_path)# Returns a Hub Dataset but does not download data locally
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Reading Samples From a Hub Dataset
Data is not immediately read into memory because Hub operates lazily. You can fetch data by calling the.numpy()method, which reads data into a numpy array.
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# Indexing
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W = ds.images[0].numpy()# Fetch an image and return a numpy array
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X = ds.labels[0].numpy(as_list=True)# Fetch a label and store it as a
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# list of numpy arrays
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# Slicing
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Y = ds.images[0:100].numpy()# Fetch 100 images and return a numpy array
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# The method above produces an exception if
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# the images are not all the same size
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Z = ds.labels[0:100].numpy(as_list=True)# Fetch 100 labels and store
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# them as a list of numpy arrays
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Congratulations, you've got Hub working on your local machine