Datasets:
Tasks:
Image Classification
Modalities:
Image
Sub-tasks:
multi-class-image-classification
Languages:
Indonesian
Size:
n<1K
License:
Dataset Preview
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[{'expected': SplitInfo(name='train', num_bytes='202.173.747', num_examples=200, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=223325607, num_examples=1400, shard_lengths=None, dataset_name='coffee-beans')}, {'expected': SplitInfo(name='test', num_bytes='28.985.470', num_examples=1400, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='test', num_bytes=32008823, num_examples=200, shard_lengths=None, dataset_name='coffee-beans')}]
Error code: UnexpectedError
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image_file_path
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End of preview.
Dataset Card for Beans
Dataset Summary
Coffee Beans Grading
Supported Tasks and Leaderboards
image-classification
: Based on a coffee bean grading, the goal of this task is to grade single beans for clusterization.
Languages
Indonesia
Dataset Structure
Data Instances
A sample from the training set is provided below:
{
'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/0aaa78294d4bf5114f58547e48d91b7826649919505379a167decb629aa92b0a/train/bean_rust/bean_rust_train.109.jpg',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x500 at 0x16BAA72A4A8>,
'labels': 1
}
Data Fields
The data instances have the following fields:
image_file_path
: astring
filepath to an image.image
: APIL.Image.Image
object containing the image. Note that when accessing the image column:dataset[0]["image"]
the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the"image"
column, i.e.dataset[0]["image"]
should always be preferred overdataset["image"][0]
.labels
: anint
classification label.
Class Label Mappings:
{
"1": 0,
"2": 1,
"3": 2,
}
Data Splits
train | validation | test | |
---|---|---|---|
# of examples | 1400 | 400 | 200 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
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Additional Information
Dataset Curators
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Licensing Information
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Citation Information
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