Quickstart
A jump-start guide to using Deep Lake.
Deep Lake can be installed through pip. By default, Deep Lake does not install dependencies for audio, video, google-cloud, and other features. Details on all installation options are available here.
$ pip3 install deeplake
Let's load the Visdrone dataset, a rich dataset with many object detections per image. Datasets hosted on Activeloop Platform are typically identified by host organization name followed by the dataset name:
activeloop/visdrone-det-train.import deeplake
dataset_path = 'hub://activeloop/visdrone-det-train'
ds = deeplake.load(dataset_path) # Returns a Deep Lake Dataset but does not download data locally
Data is not immediately read into memory because Deep Lake operates lazily. You can fetch data by calling the
.numpy() or .data() methods:# Indexing
image = ds.images[0].numpy() # Fetch the first image and return a numpy array
labels = ds.labels[0].data() # Fetch the labels in the first image
boxes = ds.boxes[0].numpy() # Fetch the bounding boxes in the first image
# Slicing
img_list = ds.labels[0:100].numpy(aslist=True) # Fetch 100 labels and store
# them as a list of numpy arrays
Other metadata such as the mapping between numerical labels and their text counterparts can be accessed using:
labels_list = ds.labels.info['class_names']
Deep Lake enables users to visualize and interpret large datasets. The tensor layout for a dataset can be inspected using:
ds.summary()
ds.visualize()
Visualizing datasets in Activeloop Platform will unlock more features and faster performance compared to visualization in Jupyter notebooks.
You can perform all of the steps above and more with your own datasets! Please check out the links below to learn more:
Congratulations, you've got Deep Lake working on your local machine
🤓
Last modified 11d ago