Fruit Object Detection - Dataset Ninja

Summary #

Fruit Object Detection is a dataset for an object detection task. Possible applications of the dataset could be in the food industry.

The dataset consists of 4474 images with 22576 labeled objects belonging to 11 different classes including pear, apple, grape, and other: pineapple, durian, korean melon, watermelon, tangerine, lemon, cantaloupe, and dragon fruit.

Images in the Fruit Object Detection dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: train (3836 images) and valid (638 images). The dataset was released in 2022.

Here are the visualized examples for the classes:

Explore #

Fruit Object Detection dataset has 4474 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

Sample annotation mask from Fruit Object Detection

Sample image from Fruit Object Detection

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Have a look at 4474 images

Because of dataset's license preview is limited to 12 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 11 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-10 of 11

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
pear➔ rectangle	638	3280	5.14	38.22%
apple➔ rectangle	557	2570	4.61	44.72%
grape➔ rectangle	494	1216	2.46	47.98%
pineapple➔ rectangle	483	1745	3.61	26.76%
durian➔ rectangle	432	2908	6.73	48.58%
korean melon➔ rectangle	388	2651	6.83	46.53%
watermelon➔ rectangle	381	1627	4.27	44.98%
tangerine➔ rectangle	359	2907	8.1	39.69%
lemon➔ rectangle	298	1205	4.04	29.73%
cantaloupe➔ rectangle	277	810	2.92	34.62%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-10 of 11

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
pear rectangle	3280	8.1%	95.39%	0.18%	13px	5%	772px	100%	111px	26.09%	19px	3.61%	856px	100%
durian rectangle	2908	8.28%	99.67%	0.09%	10px	2.4%	2623px	100%	155px	28.63%	11px	1.85%	3304px	100%
tangerine rectangle	2907	5.4%	93.43%	0.09%	12px	2.46%	1097px	96.9%	109px	21.52%	14px	2.46%	1452px	96.41%
korean melon rectangle	2651	7.79%	64.06%	0.03%	16px	1%	773px	100%	151px	25.45%	13px	2.09%	981px	100%
apple rectangle	2570	10.6%	95.38%	0.05%	13px	2.36%	3471px	98.07%	181px	30.65%	11px	1.77%	3431px	100%
pineapple rectangle	1745	7.6%	87.96%	0.13%	13px	4.5%	1299px	100%	169px	25.7%	13px	1.92%	1350px	100%
dragon fruit rectangle	1657	5.36%	85.36%	0.17%	13px	2.84%	3060px	98.38%	150px	21.79%	11px	3.33%	2312px	89.12%
watermelon rectangle	1627	11.44%	100%	0.09%	18px	2.62%	2146px	100%	208px	33.06%	20px	2.58%	2091px	100%
grape rectangle	1216	20.19%	100%	0.01%	8px	2.01%	3234px	100%	312px	49.65%	4px	0.67%	2266px	100%
lemon rectangle	1205	7.98%	96.53%	0.3%	12px	4.38%	1172px	98.3%	161px	27.55%	13px	4.55%	1127px	98.2%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 22576 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 22576

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	korean melon rectangle	0428.jpg	1200 x 1500	341px	28.42%	255px	17%	4.83%
2➔	korean melon rectangle	0428.jpg	1200 x 1500	310px	25.83%	254px	16.93%	4.37%
3➔	korean melon rectangle	0428.jpg	1200 x 1500	233px	19.42%	307px	20.47%	3.97%
4➔	korean melon rectangle	0428.jpg	1200 x 1500	352px	29.33%	293px	19.53%	5.73%
5➔	korean melon rectangle	0428.jpg	1200 x 1500	269px	22.42%	228px	15.2%	3.41%
6➔	korean melon rectangle	0428.jpg	1200 x 1500	386px	32.17%	200px	13.33%	4.29%
7➔	korean melon rectangle	0428.jpg	1200 x 1500	415px	34.58%	248px	16.53%	5.72%
8➔	pear rectangle	val0135.jpg	183 x 275	91px	49.73%	97px	35.27%	17.54%
9➔	pear rectangle	val0135.jpg	183 x 275	92px	50.27%	95px	34.55%	17.37%
10➔	cantaloupe rectangle	0098.jpg	600 x 600	343px	57.17%	324px	54%	30.87%

License #

License is unknown for the Fruit Object Detection dataset.

Source

Citation #

If you make use of the Fruit Object Detection data, please cite the following reference:

@dataset{Fruit Object Detection,
  author={},
  title={Fruit Object Detection},
  year={2022},
  url={https://www.kaggle.com/datasets/eunpyohong/fruit-object-detection}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-fruit-object-detection-dataset,
  title = { Visualization Tools for Fruit Object Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/fruit-object-detection } },
  url = { https://datasetninja.com/fruit-object-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Please visit dataset homepage to download the data.

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