Machine Learning Datasets Machine Learning Datasets
  • GitHub
  • Slack
  • Documentation
Get Started
Machine Learning Datasets Machine Learning Datasets
Get Started
Machine Learning Datasets
  • GitHub
  • Slack
  • Documentation

Docy

Machine Learning Datasets

  • Folder icon closed Folder open iconDatasets
    • MNIST
    • ImageNet Dataset
    • COCO Dataset
    • CIFAR 10 Dataset
    • CIFAR 100 Dataset
    • FFHQ Dataset
    • Places205 Dataset
    • GTZAN Genre Dataset
    • GTZAN Music Speech Dataset
    • The Street View House Numbers (SVHN) Dataset
    • Caltech 101 Dataset
    • LibriSpeech Dataset
    • dSprites Dataset
    • PUCPR Dataset
    • RAVDESS Dataset
    • GTSRB Dataset
    • CSSD Dataset
    • ATIS Dataset
    • Free Spoken Digit Dataset (FSDD)
    • not-MNIST Dataset
    • ECSSD Dataset
    • COCO-Text Dataset
    • CoQA Dataset
    • FGNET Dataset
    • ESC-50 Dataset
    • GlaS Dataset
    • UTZappos50k Dataset
    • Pascal VOC 2012 Dataset
    • Pascal VOC 2007 Dataset
    • Omniglot Dataset
    • HMDB51 Dataset
    • Chest X-Ray Image Dataset
    • NIH Chest X-ray Dataset
    • Fashionpedia Dataset
    • DRIVE Dataset
    • Kaggle Cats & Dogs Dataset
    • Lincolnbeet Dataset
    • Sentiment-140 Dataset
    • MURA Dataset
    • LIAR Dataset
    • Stanford Cars Dataset
    • SWAG Dataset
    • HASYv2 Dataset
    • WFLW Dataset
    • Visdrone Dataset
    • 11k Hands Dataset
    • QuAC Dataset
    • LFW Deep Funneled Dataset
    • LFW Funneled Dataset
    • Office-Home Dataset
    • LFW Dataset
    • PlantVillage Dataset
    • Optical Handwritten Digits Dataset
    • UCI Seeds Dataset
    • STN-PLAD Dataset
    • FER2013 Dataset
    • Adience Dataset
    • PPM-100 Dataset
    • CelebA Dataset
    • Fashion MNIST Dataset
    • Google Objectron Dataset
    • CARPK Dataset
    • CACD Dataset
    • Flickr30k Dataset
    • Kuzushiji-Kanji (KKanji) dataset
    • KMNIST
    • EMNIST Dataset
    • USPS Dataset
    • MARS Dataset
    • HICO Classification Dataset
    • NSynth Dataset
    • RESIDE dataset
    • Electricity Dataset
    • DRD Dataset
    • Caltech 256 Dataset
    • AFW Dataset
    • PACS Dataset
    • TIMIT Dataset
    • KTH Actions Dataset
    • WIDER Face Dataset
    • WISDOM Dataset
    • DAISEE Dataset
    • WIDER Dataset
    • LSP Dataset
    • UCF Sports Action Dataset
    • Wiki Art Dataset
    • FIGRIM Dataset
    • ANIMAL (ANIMAL10N) Dataset
    • OPA Dataset
    • DomainNet Dataset
    • HAM10000 Dataset
    • Tiny ImageNet Dataset
    • Speech Commands Dataset
    • 300w Dataset
    • Food 101 Dataset
    • VCTK Dataset
    • LOL Dataset
    • AQUA Dataset
    • LFPW Dataset
    • ARID Video Action dataset
    • NABirds Dataset
    • SQuAD Dataset
    • ICDAR 2013 Dataset
    • Animal Pose Dataset
  • Folder icon closed Folder open iconDeep Lake Docs Home
  • Folder icon closed Folder open iconDataset Visualization
  • API Basics
  • Storage & Credentials
  • Getting Started
  • Tutorials (w Colab)
  • Playbooks
  • Data Layout
  • Folder icon closed Folder open iconShuffling in ds.pytorch()
  • Folder icon closed Folder open iconStorage Synchronization
  • Folder icon closed Folder open iconTensor Relationships
  • Folder icon closed Folder open iconQuickstart
  • Folder icon closed Folder open iconHow to Contribute

Lincolnbeet Dataset

Estimated reading: 3 minutes

Visualization of the LincolnBeet Dataset in the Deep Lake UI

LincoInBeet 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 darknet 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 the evaluation of various object detection models in practice.

Download LincolnBeet Dataset in Python

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

Load LincolnBeet Train Subset
				
					import deeplake
ds = deeplake.load('hub://activeloop/lincolnbeet-train')
				
			
Load LincolnBeet Validation Subset
				
					import deeplake
ds = deeplake.load('hub://activeloop/lincolnbeet-val')
				
			
Load LincolnBeet Test Subset
				
					import deeplake
ds = deeplake.load('hub://activeloop/lincolnbeet-test')
				
			

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 LincolnBeet Dataset with PyTorch and TensorFlow in Python

Training LincolnBeet dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
				
			
Training LincolnBeet with TensorFlow in Python
				
					dataloader = ds.tensorflow()
				
			

Additional Information

  • Repository: https://github.com/LAR/lincolnbeet_dataset
  • Paper: Salazar-Gomez, A., Darbyshire, M., Gao, J., Sklar, E. I., & Parsons, S. (2021). Towards practical object detection for weed spraying in precision agriculture. arXiv preprint arXiv:2109.11048.
  • Point of Contact: [email protected]
Dataset Curators

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

Licensing Information

Deep Lake 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
				
					@article{salazar2021towards,
  title={Towards practical object detection for weed spraying 
  in precision agriculture},
  author={Salazar-Gomez, Adrian and Darbyshire, Madeleine and Gao, 
  Junfeng and Sklar, Elizabeth I and Parsons, Simon},
  journal={arXiv preprint arXiv:2109.11048},
  year={2021}
}
				
			
Datasets - Previous Kaggle Cats & Dogs Dataset Next - Datasets Sentiment-140 Dataset
Leaf Illustration

© 2022 All Rights Reserved by Snark AI, inc dba Activeloop