Datasets:
image
imagewidth (px) 74
6.02k
| label
class label 3
classes |
---|---|
0chicken
|
|
0chicken
|
|
2muffin
|
|
1dog
|
|
2muffin
|
|
2muffin
|
|
2muffin
|
|
1dog
|
|
1dog
|
|
0chicken
|
|
1dog
|
|
0chicken
|
|
0chicken
|
|
2muffin
|
|
2muffin
|
|
1dog
|
|
2muffin
|
|
1dog
|
|
1dog
|
|
1dog
|
|
2muffin
|
|
0chicken
|
|
1dog
|
|
2muffin
|
|
1dog
|
|
1dog
|
|
2muffin
|
|
2muffin
|
|
0chicken
|
|
0chicken
|
|
2muffin
|
|
1dog
|
|
0chicken
|
|
0chicken
|
|
2muffin
|
|
0chicken
|
|
2muffin
|
|
0chicken
|
|
1dog
|
|
2muffin
|
|
1dog
|
|
0chicken
|
|
1dog
|
|
1dog
|
|
0chicken
|
|
0chicken
|
|
0chicken
|
|
2muffin
|
|
0chicken
|
|
1dog
|
|
0chicken
|
|
0chicken
|
|
1dog
|
|
2muffin
|
|
2muffin
|
|
2muffin
|
|
0chicken
|
|
2muffin
|
|
1dog
|
|
1dog
|
|
0chicken
|
|
1dog
|
|
0chicken
|
|
0chicken
|
|
1dog
|
|
0chicken
|
|
1dog
|
|
1dog
|
|
2muffin
|
|
2muffin
|
|
2muffin
|
|
2muffin
|
|
1dog
|
|
2muffin
|
|
0chicken
|
|
1dog
|
|
2muffin
|
|
0chicken
|
|
2muffin
|
|
1dog
|
|
0chicken
|
|
1dog
|
|
0chicken
|
|
2muffin
|
|
0chicken
|
|
0chicken
|
|
2muffin
|
|
2muffin
|
|
0chicken
|
|
0chicken
|
|
0chicken
|
|
1dog
|
|
1dog
|
|
1dog
|
|
1dog
|
|
1dog
|
|
2muffin
|
|
1dog
|
|
1dog
|
|
0chicken
|
Dataset Card for the Dog 🐶 vs. Food 🍔 (a.k.a. Dog Food) Dataset
Dataset Summary
This is a dataset for multiclass image classification, between 'dog', 'chicken', and 'muffin' classes.
The 'dog' class contains images of dogs that look like fried chicken and some that look like images of muffins, while the 'chicken' and 'muffin' classes contains images of (you guessed it) fried chicken and muffins 😋
Supported Tasks and Leaderboards
TBC
Languages
The labels are in English (['dog', 'chicken', 'muffin'])
Dataset Structure
Data Instances
A sample from the training set is provided below:
{
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=300x470 at 0x7F176094EF28>,
'label': 0}
}
Data Fields
- img: A
PIL.JpegImageFile
object containing the 300x470. 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]
- label: 0-1 with the following correspondence 0 dog 1 food
Data Splits
Train (1875 images) and Test (625 images)
Dataset Creation
Curation Rationale
N/A
Source Data
Initial Data Collection and Normalization
This dataset was taken from the qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins? Github repository and randomly splitting 25% of the data for validation.
Annotations
Annotation process
This data was scraped from the internet and annotated based on the query words.
Personal and Sensitive Information
N/A
Considerations for Using the Data
Social Impact of Dataset
N/A
Discussion of Biases
This dataset is balanced -- it has an equal number of images of dogs (1000) compared to chicken (1000 and muffin (1000). This should be taken into account when evaluating models.
Other Known Limitations
N/A
Additional Information
Dataset Curators
This dataset was created by @lanceyjt, @yl3829, @wesleytao, @qw2243c and @asyouhaveknown
Licensing Information
No information is indicated on the original github repository.
Citation Information
N/A
Contributions
Thanks to @lewtun for adding this dataset.
- Downloads last month
- 475