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Fruit Object Detection Dataset

4474111
Tagfood
Taskobject detection
Release YearMade in 2022
Licenseunknown

Summary #

Dataset LinkHomepage

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.

OpenSample annotation mask from Fruit Object DetectionSample image from Fruit Object Detection
OpenSample annotation mask from Fruit Object DetectionSample image from Fruit Object Detection
OpenSample annotation mask from Fruit Object DetectionSample image from Fruit Object Detection
OpenSample annotation mask from Fruit Object DetectionSample image from Fruit Object Detection
OpenSample annotation mask from Fruit Object DetectionSample image from Fruit Object Detection
OpenSample annotation mask from Fruit Object DetectionSample image from Fruit Object Detection
OpenSample annotation mask from Fruit Object DetectionSample image from Fruit Object Detection
OpenSample annotation mask from Fruit Object DetectionSample image from Fruit Object Detection
OpenSample annotation mask from Fruit Object DetectionSample image from Fruit Object Detection
OpenSample annotation mask from Fruit Object DetectionSample image from Fruit Object Detection
OpenSample annotation mask from Fruit Object DetectionSample image from Fruit Object Detection
OpenSample annotation mask from Fruit Object DetectionSample image from Fruit Object Detection
πŸ‘€
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.

Search
Rows 1-10 of 11
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
pearβž”
rectangle
638
3280
5.14
38.22%
appleβž”
rectangle
557
2570
4.61
44.72%
grapeβž”
rectangle
494
1216
2.46
47.98%
pineappleβž”
rectangle
483
1745
3.61
26.76%
durianβž”
rectangle
432
2908
6.73
48.58%
korean melonβž”
rectangle
388
2651
6.83
46.53%
watermelonβž”
rectangle
381
1627
4.27
44.98%
tangerineβž”
rectangle
359
2907
8.1
39.69%
lemonβž”
rectangle
298
1205
4.04
29.73%
cantaloupeβž”
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.

Search
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 22576
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
korean melon
rectangle
0428.jpg
1200 x 1500
341px
28.42%
255px
17%
4.83%
2βž”
korean melon
rectangle
0428.jpg
1200 x 1500
310px
25.83%
254px
16.93%
4.37%
3βž”
korean melon
rectangle
0428.jpg
1200 x 1500
233px
19.42%
307px
20.47%
3.97%
4βž”
korean melon
rectangle
0428.jpg
1200 x 1500
352px
29.33%
293px
19.53%
5.73%
5βž”
korean melon
rectangle
0428.jpg
1200 x 1500
269px
22.42%
228px
15.2%
3.41%
6βž”
korean melon
rectangle
0428.jpg
1200 x 1500
386px
32.17%
200px
13.33%
4.29%
7βž”
korean melon
rectangle
0428.jpg
1200 x 1500
415px
34.58%
248px
16.53%
5.72%
8βž”
pear
rectangle
val0135.jpg
183 x 275
91px
49.73%
97px
35.27%
17.54%
9βž”
pear
rectangle
val0135.jpg
183 x 275
92px
50.27%
95px
34.55%
17.37%
10βž”
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 = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Please visit dataset homepage to download the data.

. . .

Disclaimer #

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