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

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

• The weed image dataset is publicly available at https://www.kaggle.com/yuzhenlu/cottonweedid15
• To prepare your own dataset, you can run python common/partition_imgs_Ubuntu.py

• 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

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}
}

• fine-tuning.pytorch
• FINETUNING TORCHVISION MODELS
• Pretrained models for Pytorch
• Pytorch-Image-Classification
• Image Similarity
• EfficientNet PyTorch
• How do I check the number of parameters of a model?