- Description:
Cassava consists of leaf images for the cassava plant depicting healthy and four (4) disease conditions; Cassava Mosaic Disease (CMD), Cassava Bacterial Blight (CBB), Cassava Greem Mite (CGM) and Cassava Brown Streak Disease (CBSD). Dataset consists of a total of 9430 labelled images. The 9430 labelled images are split into a training set (5656), a test set(1885) and a validation set (1889). The number of images per class are unbalanced with the two disease classes CMD and CBSD having 72% of the images.
Source code:
tfds.image_classification.Cassava
Versions:
0.1.0
(default): No release notes.
Download size:
1.26 GiB
Dataset size:
1.26 GiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'test' |
1,885 |
'train' |
5,656 |
'validation' |
1,889 |
- Feature structure:
FeaturesDict({
'image': Image(shape=(None, None, 3), dtype=uint8),
'image/filename': Text(shape=(), dtype=string),
'label': ClassLabel(shape=(), dtype=int64, num_classes=5),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
image | Image | (None, None, 3) | uint8 | |
image/filename | Text | string | ||
label | ClassLabel | int64 |
Supervised keys (See
as_supervised
doc):('image', 'label')
Figure (tfds.show_examples):
- Examples (tfds.as_dataframe):
- Citation:
@misc{mwebaze2019icassava,
title={iCassava 2019Fine-Grained Visual Categorization Challenge},
author={Ernest Mwebaze and Timnit Gebru and Andrea Frome and Solomon Nsumba and Jeremy Tusubira},
year={2019},
eprint={1908.02900},
archivePrefix={arXiv},
primaryClass={cs.CV}
}