Dataset Ninja LogoDataset Ninja:

MineralImage5k Dataset

1920761
Tagscience
Taskobject detection
Release YearMade in 2023
LicenseMIT
Download4 GB

Introduction #

Released 2023-08-01 ·Sergey Nesteruk, Julia Agafonova, Igor Pavlovet al.

The authors of the MineralImage5k dataset make a significant contribution by introducing an open benchmark for zero-shot raw mineral visual recognition. The dataset consits of more than 5 thousand mineral species, covering almost the span of existing minerals on the Earth. Beyond the zero-shot classification dataset, subsets are provided for segmentation, mineral size estimation, and few-shot classification. Baseline solutions for these computer vision problems are published, inviting the community to surpass them.

Importance of Mineral Diagnostics

Nature boasts about 6000 known minerals and their varieties, with only a fraction being rock-forming or of industrial interest. Accurate mineral diagnostics is crucial for geological work, enabling geological mapping and deposit exploration. However, exact mineral identification is a complex and time-consuming task, requiring significant skill and time investment by geologists. The involvement of machine intelligence in visual diagnostics aims to optimize this process, freeing up professional mineralogists’ time for more complex tasks and identifying errors in visual diagnostics.

The integration of machine intelligence in mineral diagnostics, particularly in raw samples, holds promising implications. If an algorithm can enable visual diagnostics of minerals at the level of an ordinary geologist, it opens avenues for creating search robots for exploring challenging terrains on Earth and potentially on other planets. The addition of visual diagnostics capabilities complements existing methods like IR spectroscopy and Raman spectroscopy, enhancing the selection of objects for analysis and expanding research possibilities.

Dataset Collection and preprocessing

For dataset creation, the authors utilized the Fersman mineralogical museum image set, one of the largest mineralogical collections globally, containing over 170000 samples of about 5000 mineral species. The dataset provides a diverse and representative view of mineral diversity, offering advantages over other datasets.

image

The authors implemented a comprehensive image preprocessing pipeline to enhance the dataset’s quality and representation. This involved removing uninformative images, handling duplicates, and resizing images to a standardized format. The pipeline also addressed potential issues related to text on images, distinguishing between minerals, reference cubes, and text plates.

Formation of Multiple Datasets

The filtered and cropped images were used to create multiple datasets, including a zero-shot classification dataset and subsets for few-shot classification. Additionally, auxiliary datasets for segmentation and mineral size estimation were provided, featuring manually labeled mineral and satellite masks and manually measured mineral sizes, respectively.

Classes Images Minimum images per class Task Subsets
5139 44784 1 Classification Test
360 31982 17 Classification Train&Test
98 23496 78 Classification Train&Test
36 16001 187 Classification Train&Test
10 8549 462 Classification Train&Test
>100 Segmentation Test
18076 Regression (size estimation) Test

The proposed subsets with different classes definition.

ExpandExpand
Dataset LinkHomepageDataset LinkResearch Paper

Summary #

MineralImage5k: A Benchmark for Zero-Shot Raw Mineral Visual Recognition and Description is a dataset for object detection, semantic segmentation, and classification tasks. It is used in the geological research.

The dataset consists of 19207 images with 31393 labeled objects belonging to 6 different classes including stone, rock, mineral, and other: gem, crystal, and mineral ore.

Images in the MineralImage5k dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 8 splits in the dataset: 1_syst (15005 images), 7_stepanov (1361 images), 5_PDK (1057 images), 3_op (745 images), 2_mest (561 images), 4_cryst (220 images), 10_meteor (151 images), and segm (107 images). Additionally, the images have following tags: name, description (dsc), size_sm. The dataset was released in 2023 by the Sber AI, Russia, Artificial Intelligence Research Institute, Russia, and Fersman Mineralogical Museum, Russia.

Here is the visualized example grid with annotations:

Explore #

MineralImage5k dataset has 19207 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 MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
OpenSample annotation mask from MineralImage5kSample image from MineralImage5k
👀
Have a look at 19207 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 6 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 1-6 of 6
Class
Images
Objects
Count on image
average
Area on image
average
stone
rectangle
15486
21630
1.4
40.05%
rock
rectangle
2874
3211
1.12
38.68%
mineral
any
2279
3306
1.45
26.58%
gem
rectangle
1223
1728
1.41
20.53%
crystal
rectangle
656
766
1.17
33.08%
mineral ore
rectangle
419
752
1.79
14.72%

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.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

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 1-6 of 6
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
stone
rectangle
21630
29.02%
99.9%
0.24%
31px
4.69%
1011px
100%
385px
49.91%
36px
3.52%
1023px
99.9%
mineral
any
3306
18.92%
94.58%
0.28%
35px
3.8%
940px
99.8%
285px
40.21%
43px
4.2%
1020px
100%
rock
rectangle
3211
34.87%
99.9%
0.47%
37px
7.62%
1003px
100%
409px
56.13%
60px
5.86%
1023px
99.9%
gem
rectangle
1728
14.87%
93.68%
0.15%
30px
4.4%
978px
96.89%
270px
36%
36px
3.52%
982px
98.37%
crystal
rectangle
766
29.81%
93.37%
0.64%
59px
7.6%
1017px
99.86%
545px
60.03%
65px
6.54%
1014px
99.35%
mineral ore
rectangle
752
8.57%
99.66%
0.15%
28px
4.69%
990px
100%
182px
25.42%
30px
2.93%
1023px
99.9%

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 31393 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 31393
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
mineral
any
FMM_7_504.JPG
542 x 592
520px
95.94%
555px
93.75%
52.43%
2
mineral
any
FMM_1_5507.JPG
518 x 732
497px
95.95%
667px
91.12%
20.44%
3
mineral
any
FMM_1_87323.JPG
595 x 490
529px
88.91%
321px
65.51%
36.08%
4
mineral
any
FMM_7_6588.JPG
537 x 591
518px
96.46%
345px
58.38%
39.31%
5
mineral
any
FMM_1_8223.JPG
508 x 807
498px
98.03%
756px
93.68%
52.8%
6
mineral
any
FMM_1_93978.JPG
415 x 671
407px
98.07%
662px
98.66%
73.06%
7
mineral
any
FMM_4_5290.JPG
210 x 489
174px
82.86%
251px
51.33%
33.53%
8
mineral
any
FMM_1_94854.JPG
382 x 563
367px
96.07%
547px
97.16%
65.03%
9
mineral
any
FMM_7_5717.JPG
567 x 870
512px
90.3%
605px
69.54%
11.81%
10
mineral
any
FMM_1_86636.JPG
520 x 592
511px
98.27%
587px
99.16%
73.62%

License #

MineralImage5k: A Benchmark for Zero-Shot Raw Mineral Visual Recognition and Description is under MIT license.

Source

Citation #

If you make use of the MineralImage5k data, please cite the following reference:

@dataset{MineralImage5k,
  author={Sergey Nesteruk and Julia Agafonova and Igor Pavlov and Maxim Gerasimov and Nikolay Latyshev and Denis Dimitrov and Andrey Kuznetsov and Artur Kadurin and Pavel Plechov},
  title={MineralImage5k: A Benchmark for Zero-Shot Raw Mineral Visual Recognition and Description},
  year={2023},
  url={https://github.com/ai-forever/mineral-recognition}
}

Source

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

@misc{ visualization-tools-for-mineral-image-5k-dataset,
  title = { Visualization Tools for MineralImage5k Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/mineral-image-5k } },
  url = { https://datasetninja.com/mineral-image-5k },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset MineralImage5k can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='MineralImage5k', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

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