Load the Office-Home Dataset for domain adaptation in Python with one line of code in seconds and plug it in TensorFlow and PyTorch with Activeloop Hub.
The Office-Home dataset was created to assess deep learning algorithms for domain adaptation-based object recognition. The dataset consists of images from 4 different domains which include art, clip art, product, and Real-World images. The dataset contains images of 65 types of objects commonly found in Office-Home Settings.
Download Office-Home Dataset in Python
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Train a model on Office-Home Dataset with TensorFlow in Python
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dataloader = ds.tensorflow()
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Office-Home Dataset Creation
Data Collection and Normalization Information
Python crawler was used for image collection. There were 100,000 images of 120 different objects. To make sure that the right objects are present in the image, the dataset was cleaned. It was also ensured that each category has a certain number of images. The last version of the dataset has 15,500 images of 65 different objects.
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty and Sethuraman Panchanathan
Office-Home Dataset Licensing Information
More information about the license can be found here. Hub users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license. If you're a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!
Office-Home Dataset Citation Information
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@inproceedings{venkateswara2017deep,
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title={Deep hashing network for unsupervised domain adaptation},
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author={Venkateswara, Hemanth and Eusebio, Jose and Chakraborty, Shayok and Panchanathan, Sethuraman},
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booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
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pages={5018--5027},
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year={2017} }
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Office-Home Dataset FAQs
What is the Office-Home dataset for Python?
The Office-Home dataset was developed to assess domain adaptation algorithms for object recognition using deep learning. The dataset is made up of images from four different domains—artistic, product, real-world images, and clip art. A Python web-crawler that crawled through several search engines and online image directories was used to collect the images in the dataset.
What is the Office-Home dataset used for?
The Office-Home dataset is used as a benchmark dataset for domain adaptation. It contains four domains where each domain consists of 65 categories. The four domains include art (a collection of artistic images in the form of sketches), clipart (a collection of clipart images), product (a domain containing images of objects without a background), and real-world images (a domain containing images of objects captured with a regular camera).
How to download the Office-Home dataset in Python?
With the open-source package Activeloop Hub in Python you can load the Office-Home dataset fast with one line of code. See detailed instructions on how to load the Office-Home dataset in Python.
How can I use Office-Home dataset in PyTorch or TensorFlow?