Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems.

Related tags

Deep Learning CottonWeeds
Overview

CottonWeeds

Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems.

requirements

  • pytorch
  • torchsummary
  • tensorboard
  • PIL
  • Scikit-learn

Dataset

Usage

  • To train the models, just specify the name of the models, and then run python train.py.
  • To test the images, just specify the name of the models, and then run python test.py.
  • To eval new data, just specify the name of the models, and then run python eval.py.
  • To visualize the training, run tensorboard --logdir=runs

Citation

Detailed documentation of deep transfer learning for weed classification of the cotton weed dataset is given in our arXiv paper: https://arxiv.org/abs/2110.04960. If you use the dataset or models in a publication, please cite this paper.

@misc{chen2021performance,
      title={Performance Evaluation of Deep Transfer Learning on Multiclass Identification of Common Weed Species in Cotton Production Systems}, 
      author={Dong Chen, Yuzhen Lu, Zhaojiang Li, Sierra Young},
      year={2021},
      eprint={2110.04960},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Reference

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Comments
  • mobilenet model selection

    mobilenet model selection

    In compute_sim.py there's a reference to a models directory for the model path that isn't in the repository that I can see: EVAL_MODEL = '../../models/' + model_name + '_' + str(args.seeds) + ".pth"

    Did you use your model trained on the CottonWeeds dataset or the ImageNet-only model for the feature extraction and cosine similarity method as per the original repo (in model_util.py)?

    opened by geezacoleman 3
Owner
Dong Chen
Dong Chen
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