README.md

metadata

license: cc-by-4.0
dataset_info:
  features:
    - name: image_id
      dtype: int64
    - name: image
      dtype: image
    - name: width
      dtype: int64
    - name: height
      dtype: int64
    - name: objects
      struct:
        - name: id
          sequence: int64
        - name: area
          sequence: int64
        - name: bbox
          sequence:
            sequence: float32
        - name: category
          sequence: string
  splits:
    - name: train
      num_bytes: 905619617.284
      num_examples: 2342
    - name: test
      num_bytes: 73503583
      num_examples: 236
  download_size: 991825068
  dataset_size: 979123200.284
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
task_categories:
  - object-detection

This Dataset is created from processing the files from this GitHub repository : PlantDoc-Object-Detection-Dataset

@inproceedings{10.1145/3371158.3371196, author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun}, title = {PlantDoc: A Dataset for Visual Plant Disease Detection}, year = {2020}, isbn = {9781450377386}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3371158.3371196}, doi = {10.1145/3371158.3371196}, booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD}, pages = {249–253}, numpages = {5}, keywords = {Deep Learning, Object Detection, Image Classification}, location = {Hyderabad, India}, series = {CoDS COMAD 2020} }