Datasets:
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} }