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Lincolnbeet Dataset
Load Lincolnbeet fast in Python. Lincolnbeet is a weed, plants & beets dataset. Stream Lincolnbeet while training in PyTorch & TensorFlow. Visualize Lincolnbeet.
Visualization of Lincolnbeet Dataset on the Activeloop Platform

LincolnBeet Dataset

What is LincolnBeet Dataset

The Lincolnbeet dataset includes 4402 images (1920 x 1080 pixels) containing weed, plants and sugar beets, as well as object detection labels. The labels are provided in COCOjson, XML, and darknets formats. The Lincolnbeet dataset is an object detection dataset created to facilitate the development of methods to identify objects in an environment with a high level of occlusion. In addition, the dataset was introduced to encourage evaluation of various object detection models in practice.

Downloading LincolnBeet Dataset in Python

Instead of downloading the LincolnBeet, you can effortlessly load it in Python via our open-source package Hub just one line of code

Load LincolnBeet Train Subset

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import hub
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ds = hub.load('hub://activeloop/lincolnbeet-train')
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Load LincolnBeet Validation Subset

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import hub
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ds = hub.load('hub://activeloop/lincolnbeet-val')
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Load LincolnBeet Test Subset

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import hub
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ds = hub.load('hub://activeloop/lincolnbeet-test')
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LincolnBeet Dataset Structure

Data Fields

  • image: a tensor containing 1920 x 1080 pixel images.
  • boxes: a tensor to draw bounding boxes around weed and sugar beet.
  • labels: a class label tensor classifying object as "weed" or "sugar_beet".

Data Splits

  • LincolnBeet training split comprises 3080 images.
  • LincolnBeet testing split comprises 880 images.
  • LincolnBeet validation split comprises 440 images.

Dataset Characteristics

  • Number of identified objects: 39246
    • Total number of Sugar Beet Identified: 16399
    • Total Weed Plants identified: 22847
  • The average percentage of the bounding box that is occluded is 0.0176
  • The average area of the image occupied by bounding boxes is 0.0717

How to use LiconlnBeet Dataset with PyTorch and TensorFlow in Python

Training LincolnBeet with PyTorch in Python

Let's use Hub's built-in PyTorch one-line dataloader to connect the data to the compute:
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dataloader = ds.pytorch(num_workers = 2, shuffle = False, batch_size= 4)
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Training LincolnBeet with TensorFlow in Python

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ds_tensorflow = ds.tensorflow()
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Additional Information

Dataset Curators

Salazar-Gomez, Adrian and Darbyshire, Madeleine and Gao, Junfeng and Sklar, Elizabeth I and Parsons, Simon

Licensing Information

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!

Citation Information

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@article{salazar2021towards,
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title={Towards practical object detection for weed spraying
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in precision agriculture},
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author={Salazar-Gomez, Adrian and Darbyshire, Madeleine and Gao,
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Junfeng and Sklar, Elizabeth I and Parsons, Simon},
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journal={arXiv preprint arXiv:2109.11048},
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year={2021}
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}
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