Project-AgML/VegNet_quality_classification

1---2dataset_info:3  features:4  - name: image5    dtype: image6  - name: label7    dtype:8      class_label:9        names:10          '0': Damaged11          '1': Dried12          '2': Old13          '3': Ripe14          '4': Unripe15  - name: crop_type16    dtype:17      class_label:18        names:19          '0': Bell Pepper20          '1': Chile Pepper21          '2': New Mexico Green Chile22          '3': Tomato23  splits:24  - name: train25    num_bytes: 14420955126    num_examples: 615027  download_size: 13184125028  dataset_size: 14420955129configs:30- config_name: default31  data_files:32  - split: train33    path: data/train-*34license: cc-by-4.035task_categories:36- image-classification37size_categories:38- 1K<n<10K39---40 41# VegNet Quality Classification42 43A dataset for quality classification of various crops. The dataset contains 6,150 images across 5 classes: Damaged, Dried, Old, Ripe, Unripe.  44Images per class:45- Damaged: 31746- Dried: 1,38947- Old: 2,04448- Ripe: 1,78749- Unripe: 61350 51This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.52 53## Citation54 55```bibtex56@article{suryawanshi2022vegnet,57  title={VegNet: dataset of vegetable quality images for machine learning applications},58  author={Suryawanshi, Yogesh and Patil, Kailas and Chumchu, Prawit},59  journal={Data in Brief},60  volume={45},61  pages={108657},62  year={2022},63  publisher={Elsevier}64}65```66 67Suryawanshi, Yogesh; PATIL, Kailas; Chumchu, Prawit (2022), “VegNet: Vegetable Dataset with quality (Unripe, Ripe, Old, Dried and Damaged)”, Mendeley Data, V1, doi: 10.17632/6nxnjbn9w6.1