Benchmark Suite (v1.1.0)

We have compiled a large suite of benchmark datasets. For reproducibility, the datasets and label vectors are versioned.

Version 1.1.0 of the Benchmark Suite for Clustering Algorithms consists of nine benchmark batteries. The five recommended ones:

  1. wut 🌟,

  2. sipu 🌟,

  3. fcps 🌟,

  4. graves 🌟,

  5. other 🌟,

and four additional ones:

  1. uci,

  2. mnist,

  3. g2mg,

  4. h2mg.

Each battery consists of several datasets of different origins. When referring to a particular benchmark problem, we use the convention “battery/dataset”, e.g, “wut/x2”. Each dataset represents n points in \(\mathbb{R}^d\) and is accompanied by at least one reference partition of cardinality k (a listing follows). The distribution of cluster sizes is summarised below by means of the Gini index g, where \(g=0\) means that all clusters consist of the same number of points.

Important

The versioned snapshots of the suite are available for download at: https://github.com/gagolews/clustering-data-v1/releases/tag/v1.1.0.

All files can be browsed at: https://github.com/gagolews/clustering-data-v1/tree/v1.1.0.

The datasets and the corresponding ground truth labels can be browsed in the Explore Datasets (v1.1.0) section.

For the results generated by different clustering algorithms, see the Clustering Results Repository (v1.1.0) section.

See https://genieclust.gagolewski.com/ and [Gag22b, GBC21] for example studies featuring different versions of this suite.

The datasets are provided solely for research purposes, unless stated otherwise. As mentioned in the File Format Specification section, each dataset is accompanied by a text file specifying more details thereon (e.g., the literature references that we are asked to cite).

As a courtesy, please cite the current project [Gag22a] as well as mention [G+22] which gives the exact version and URL of the dataset suite. Thank you.

There is some inherent overlap between the original databases. We have tried to resolve any conflicts in the best possible manner. Some datasets are equipped with additional reference labellings that did not appear in the original setting.

wut

22 datasets in \(\mathbb{R}^2\) or \(\mathbb{R}^3\) authored by the fantastic students of Marek’s 2016/2017 courses on Data Analysis in R and Python at the Faculty of Mathematics and Information Science, Warsaw University of Technology: Anna Gierlak, Eliza Kaczorek, Mateusz Kobyłka, Przemysław Kosewski, Jędrzej Krauze, Michał Maciąg, Aleksander Truszczyński, and Adam Wawrzeńczyk. Thanks!

dataset

n

d

reference labels

k

noise points

g

1

circles

4000

2

labels0

4

0

0

2

cross

2000

2

labels0

4

0

0

3

graph

2500

2

labels0

10

0

0

4

isolation

9000

2

labels0

3

0

0

5

labirynth

3546

2

labels0

6

0

0.5

6

mk1

300

2

labels0

3

0

0

7

mk2

1000

2

labels0

2

0

0

8

mk3

600

3

labels0

3

0

0

9

mk4

1500

3

labels0

3

0

0

10

olympic

5000

2

labels0

5

0

0

11

smile

1000

2

labels0

6

0

0.4

labels1

4

0

0.4

12

stripes

5000

2

labels0

2

0

0

13

trajectories

10000

2

labels0

4

0

0

14

trapped_lovers

5000

3

labels0

3

0

0.4

15

twosplashes

400

2

labels0

2

0

0

16

windows

2977

2

labels0

5

0

0.58

17

x1

120

2

labels0

3

0

0.17

18

x2

120

2

labels0

3

0

0.17

labels1

4

10

0.35

19

x3

185

2

labels0

4

0

0.21

labels1

3

0

0.42

20

z1

192

2

labels0

3

0

0

21

z2

900

2

labels0

5

0

0.58

22

z3

1000

2

labels0

4

0

0.33

sipu

An excellent battery of 20 diverse datasets created/compiled/maintained by P. Fränti and his colleagues and research students from the University of Eastern Finland. Available for download from https://cs.joensuu.fi/sipu/datasets/; see [FS18] for discussion:

We have not included the G2 sets as we suggest the cluster variances should be corrected for space dimensionality; see g2mg for an alternative. Birch3 is not included as no ground-truth labels were provided. We excluded the DIM-sets as they are too easy for most algorithms.

dataset

n

d

reference labels

k

noise points

g

1

a1

3000

2

labels0

20

0

0

2

a2

5250

2

labels0

35

0

0

3

a3

7500

2

labels0

50

0

0

4

aggregation

788

2

labels0

7

0

0.45

5

birch1

100000

2

labels0

100

0

0.01

6

birch2

100000

2

labels0

100

0

0

7

compound

399

2

labels0

6

0

0.44

labels1

4

0

0.41

labels2

5

50

0.48

labels3

4

50

0.42

labels4

5

0

0.48

8

d31

3100

2

labels0

31

0

0

9

flame

240

2

labels0

2

0

0.28

labels1

2

12

0.27

10

jain

373

2

labels0

2

0

0.48

11

pathbased

300

2

labels0

3

0

0.06

labels1

4

0

0.2

12

r15

600

2

labels0

15

0

0

labels1

9

0

0.4

labels2

8

0

0.47

13

s1

5000

2

labels0

15

0

0.03

14

s2

5000

2

labels0

15

0

0.03

15

s3

5000

2

labels0

15

0

0.03

16

s4

5000

2

labels0

15

0

0.03

17

spiral

312

2

labels0

3

0

0.02

18

unbalance

6500

2

labels0

8

0

0.63

19

worms_2

105600

2

labels0

35

0

0.28

20

worms_64

105000

64

labels0

25

0

0

fcps

9 datasets from the Fundamental Clustering Problem Suite proposed by A. Ultsch [Ult05] from the Marburg University, Germany.

Each dataset consists of 212–4096 observations in 2–3 dimensions. The GolfBall dataset is not included as it has no cluster structure. Tetragonula and Leukemia are also not part of our suite as they are given as distance matrices.

Data were originally available from elsewhere, but now can be accessed, e.g., via the R package FCPS; see also [TU20].

dataset

n

d

reference labels

k

noise points

g

1

atom

800

3

labels0

2

0

0

2

chainlink

1000

3

labels0

2

0

0

3

engytime

4096

2

labels0

2

0

0

labels1

2

0

0

4

hepta

212

3

labels0

7

0

0.01

5

lsun

400

2

labels0

3

0

0.25

6

target

770

2

labels0

6

0

0.79

labels1

2

12

0.04

7

tetra

400

3

labels0

4

0

0

8

twodiamonds

800

2

labels0

2

0

0

9

wingnut

1016

2

labels0

2

0

0

graves

10 synthetic data sets discussed by D. Graves and W. Pedrycz in [GP10].

Each dataset consists of 200–1050 observations in 2 dimensions. Originally, they come with no reference labels.

dataset

n

d

reference labels

k

noise points

g

1

dense

200

2

labels0

2

0

0

2

fuzzyx

1000

2

labels0

5

0

0.06

labels1

2

138

0.01

labels2

4

126

0.03

labels3

2

135

0.01

labels4

2

130

0.03

3

line

250

2

labels0

2

0

0.6

4

parabolic

1000

2

labels0

2

0

0.02

labels1

4

0

0.04

5

ring

1000

2

labels0

2

0

0

6

ring_noisy

1050

2

labels0

2

43

0

7

ring_outliers

1030

2

labels0

5

0

0.71

labels1

2

30

0

8

zigzag

250

2

labels0

3

0

0.4

labels1

5

0

0.04

9

zigzag_noisy

300

2

labels0

3

38

0.41

labels1

5

38

0.01

10

zigzag_outliers

280

2

labels0

3

30

0.4

labels1

5

30

0.04

other

Datasets from multiple sources:

  • chameleon_t4_8k, chameleon_t5_8k, chameleon_t7_10k, chameleon_t8_8k – datasets supposedly related to the CHAMELEON algorithm by G. Karypis et al. [KHK99].

    Source: http://glaros.dtc.umn.edu/gkhome/cluto/cluto/download

    In fact, in [KHK99] only two of the above (and some other ones) datasets are studied: chameleon_t7_10k is referred to as DS3, whilst chameleon_t8_8k is nicknamed DS4. The DS2 set mentioned therein looks like a more noisy version of fcps/twodiamonds.

  • hdbscan – a dataset used for demonstrating the outputs of the hdbscan package for Python [MHA17];

  • iris, iris5 - “the” (see [BKK+99] for discussion) famous Iris [Fis36] dataset and its imbalanced version considered in [GBC16];

  • square – a dataset of unknown/unconfirmed origin (🚧 help needed 🚧).

dataset

n

d

reference labels

k

noise points

g

1

chameleon_t4_8k

8000

2

labels0

6

761

0.25

2

chameleon_t5_8k

8000

2

labels0

6

1187

0.03

3

chameleon_t7_10k

10000

2

labels0

9

926

0.47

4

chameleon_t8_8k

8000

2

labels0

8

346

0.37

5

hdbscan

2309

2

labels0

6

510

0.18

6

iris

150

4

labels0

3

0

0

7

iris5

105

4

labels0

3

0

0.43

8

square

1000

2

labels0

2

0

0

uci

A selection of 8 high-dimensional datasets available at the UCI (University of California, Irvine) Machine Learning Repository [DG22]. Some of them were considered for benchmark purposes in, amongst others, [GP10]. They are also listed in the sipu battery. However, their original purpose is for testing classification, not clustering algorithms.

dataset

n

d

reference labels

k

noise points

g

1

ecoli

336

7

labels0

8

0

0.65

2

glass

214

9

labels0

6

0

0.48

3

ionosphere

351

34

labels0

2

0

0.28

4

sonar

208

60

labels0

2

0

0.07

5

statlog

2310

19

labels0

7

0

0

6

wdbc

569

30

labels0

2

0

0.25

7

wine

178

13

labels0

3

0

0.13

8

yeast

1484

8

labels0

10

0

0.63

mnist

This battery features two large, high-dimensional datasets:

  1. MNIST – a database of handwritten digits (a preprocessed remix of NIST data made by Y. LeCun, C. Cortes, and C.J.C. Burges),

  2. Fashion-MNIST – a similarly-structured dataset of Zalando articles compiled by H. Xiao, K. Rasul, and R. Vollgraf; see [XRV17].

Both datasets consist of 70,000 flattened 28x28 grayscale images (train and test samples combined).

dataset

n

d

reference labels

k

noise points

g

1

digits

70000

784

labels0

10

0

0.03

2

fashion

70000

784

labels0

10

0

0

g2mg

Each dataset consists of 2,048 observations from two equisized Gaussian clusters in \(d=1, 2, \dots, 128\) dimensions (the components are sampled independently from a normal distribution).

They can be considered a modified version of Gaussian G2-sets from https://cs.joensuu.fi/sipu/datasets/, but with variances dependent on datasets’ dimensionalities, i.e., \(s\sqrt{d/2}\) for different s. This makes these new problems more difficult than their original counterparts. The 1-dimensional datasets as well as those of very low and very high variances should probably be discarded.

It is well-known that such a data distribution (multivariate normal with independent components) is subject to the so-called curse of dimensionality, leading to some weird behaviour for high d; see, e.g., the Gaussian Annulus Theorem mentioned in [BHK20].

Generator: https://github.com/gagolews/clustering-data-v1/blob/master/.devel/generate_gKmg.py

We suggest that these datasets should be studied separately from other batteries, because they are too plentiful. Also, parametric algorithms that specialise in detecting Gaussian blobs (k-means, expectation-maximisation (EM) for Gaussian mixtures) will naturally perform better thereon than the non-parametric approaches.

dataset

n

d

reference labels

k

noise points

g

1

g2mg_1_10

2048

1

labels0

2

0

0

labels1

2

0

0

2

g2mg_1_20

2048

1

labels0

2

0

0

labels1

2

0

0

3

g2mg_1_30

2048

1

labels0

2

0

0

labels1

2

0

0.01

4

g2mg_1_40

2048

1

labels0

2

0

0

labels1

2

0

0.01

5

g2mg_1_50

2048

1

labels0

2

0

0

labels1

2

0

0.01

6

g2mg_1_60

2048

1

labels0

2

0

0

labels1

2

0

0.02

7

g2mg_1_70

2048

1

labels0

2

0

0

labels1

2

0

0.01

8

g2mg_1_80

2048

1

labels0

2

0

0

labels1

2

0

0

9

g2mg_1_90

2048

1

labels0

2

0

0

labels1

2

0

0

10

g2mg_2_10

2048

2

labels0

2

0

0

labels1

2

0

0

11

g2mg_2_20

2048

2

labels0

2

0

0

labels1

2

0

0

12

g2mg_2_30

2048

2

labels0

2

0

0

labels1

2

0

0

13

g2mg_2_40

2048

2

labels0

2

0

0

labels1

2

0

0.01

14

g2mg_2_50

2048

2

labels0

2

0

0

labels1

2

0

0.01

15

g2mg_2_60

2048

2

labels0

2

0

0

labels1

2

0

0

16

g2mg_2_70

2048

2

labels0

2

0

0

labels1

2

0

0.01

17

g2mg_2_80

2048

2

labels0

2

0

0

labels1

2

0

0.01

18

g2mg_2_90

2048

2

labels0

2

0

0

labels1

2

0

0.02

19

g2mg_4_10

2048

4

labels0

2

0

0

labels1

2

0

0

20

g2mg_4_20

2048

4

labels0

2

0

0

labels1

2

0

0

21

g2mg_4_30

2048

4

labels0

2

0

0

labels1

2

0

0

22

g2mg_4_40

2048

4

labels0

2

0

0

labels1

2

0

0

23

g2mg_4_50

2048

4

labels0

2

0

0

labels1

2

0

0

24

g2mg_4_60

2048

4

labels0

2

0

0

labels1

2

0

0

25

g2mg_4_70

2048

4

labels0

2

0

0

labels1

2

0

0.02

26

g2mg_4_80

2048

4

labels0

2

0

0

labels1

2

0

0.01

27

g2mg_4_90

2048

4

labels0

2

0

0

labels1

2

0

0.01

28

g2mg_8_10

2048

8

labels0

2

0

0

labels1

2

0

0

29

g2mg_8_20

2048

8

labels0

2

0

0

labels1

2

0

0

30

g2mg_8_30

2048

8

labels0

2

0

0

labels1

2

0

0

31

g2mg_8_40

2048

8

labels0

2

0

0

labels1

2

0

0.01

32

g2mg_8_50

2048

8

labels0

2

0

0

labels1

2

0

0.01

33

g2mg_8_60

2048

8

labels0

2

0

0

labels1

2

0

0.01

34

g2mg_8_70

2048

8

labels0

2

0

0

labels1

2

0

0.02

35

g2mg_8_80

2048

8

labels0

2

0

0

labels1

2

0

0.03

36

g2mg_8_90

2048

8

labels0

2

0

0

labels1

2

0

0.03

37

g2mg_16_10

2048

16

labels0

2

0

0

labels1

2

0

0

38

g2mg_16_20

2048

16

labels0

2

0

0

labels1

2

0

0

39

g2mg_16_30

2048

16

labels0

2

0

0

labels1

2

0

0

40

g2mg_16_40

2048

16

labels0

2

0

0

labels1

2

0

0

41

g2mg_16_50

2048

16

labels0

2

0

0

labels1

2

0

0

42

g2mg_16_60

2048

16

labels0

2

0

0

labels1

2

0

0.01

43

g2mg_16_70

2048

16

labels0

2

0

0

labels1

2

0

0.01

44

g2mg_16_80

2048

16

labels0

2

0

0

labels1

2

0

0.02

45

g2mg_16_90

2048

16

labels0

2

0

0

labels1

2

0

0.02

46

g2mg_32_10

2048

32

labels0

2

0

0

labels1

2

0

0

47

g2mg_32_20

2048

32

labels0

2

0

0

labels1

2

0

0

48

g2mg_32_30

2048

32

labels0

2

0

0

labels1

2

0

0

49

g2mg_32_40

2048

32

labels0

2

0

0

labels1

2

0

0.01

50

g2mg_32_50

2048

32

labels0

2

0

0

labels1

2

0

0

51

g2mg_32_60

2048

32

labels0

2

0

0

labels1

2

0

0

52

g2mg_32_70

2048

32

labels0

2

0

0

labels1

2

0

0.02

53

g2mg_32_80

2048

32

labels0

2

0

0

labels1

2

0

0.02

54

g2mg_32_90

2048

32

labels0

2

0

0

labels1

2

0

0.02

55

g2mg_64_10

2048

64

labels0

2

0

0

labels1

2

0

0

56

g2mg_64_20

2048

64

labels0

2

0

0

labels1

2

0

0

57

g2mg_64_30

2048

64

labels0

2

0

0

labels1

2

0

0.01

58

g2mg_64_40

2048

64

labels0

2

0

0

labels1

2

0

0.01

59

g2mg_64_50

2048

64

labels0

2

0

0

labels1

2

0

0.01

60

g2mg_64_60

2048

64

labels0

2

0

0

labels1

2

0

0.01

61

g2mg_64_70

2048

64

labels0

2

0

0

labels1

2

0

0

62

g2mg_64_80

2048

64

labels0

2

0

0

labels1

2

0

0.01

63

g2mg_64_90

2048

64

labels0

2

0

0

labels1

2

0

0.01

64

g2mg_128_10

2048

128

labels0

2

0

0

labels1

2

0

0

65

g2mg_128_20

2048

128

labels0

2

0

0

labels1

2

0

0

66

g2mg_128_30

2048

128

labels0

2

0

0

labels1

2

0

0

67

g2mg_128_40

2048

128

labels0

2

0

0

labels1

2

0

0

68

g2mg_128_50

2048

128

labels0

2

0

0

labels1

2

0

0.01

69

g2mg_128_60

2048

128

labels0

2

0

0

labels1

2

0

0.02

70

g2mg_128_70

2048

128

labels0

2

0

0

labels1

2

0

0.02

71

g2mg_128_80

2048

128

labels0

2

0

0

labels1

2

0

0.01

72

g2mg_128_90

2048

128

labels0

2

0

0

labels1

2

0

0.01

h2mg

Two Gaussian-like hubs of equal sizes, with spread dependent on datasets’ dimensionalities. Each dataset consists of 2,048 observations in 1, 2, …, 128 dimensions. Each point is sampled from a sphere centred at its own cluster’s centre, of radius that follows the Gaussian distribution with a predefined scaling parameter.

Generator: https://github.com/gagolews/clustering-data-v1/blob/master/.devel/generate_hKmg.py

Just like in the case of g2mg, we suggest that these datasets should be studied separately from other batteries.

dataset

n

d

reference labels

k

noise points

g

1

h2mg_1_10

2048

1

labels0

2

0

0

labels1

2

0

0

2

h2mg_1_20

2048

1

labels0

2

0

0

labels1

2

0

0.01

3

h2mg_1_30

2048

1

labels0

2

0

0

labels1

2

0

0.09

4

h2mg_1_40

2048

1

labels0

2

0

0

labels1

2

0

0.22

5

h2mg_1_50

2048

1

labels0

2

0

0

labels1

2

0

0.32

6

h2mg_1_60

2048

1

labels0

2

0

0

labels1

2

0

0.41

7

h2mg_1_70

2048

1

labels0

2

0

0

labels1

2

0

0.47

8

h2mg_1_80

2048

1

labels0

2

0

0

labels1

2

0

0.53

9

h2mg_1_90

2048

1

labels0

2

0

0

labels1

2

0

0.57

10

h2mg_2_10

2048

2

labels0

2

0

0

labels1

2

0

0

11

h2mg_2_20

2048

2

labels0

2

0

0

labels1

2

0

0

12

h2mg_2_30

2048

2

labels0

2

0

0

labels1

2

0

0.01

13

h2mg_2_40

2048

2

labels0

2

0

0

labels1

2

0

0

14

h2mg_2_50

2048

2

labels0

2

0

0

labels1

2

0

0

15

h2mg_2_60

2048

2

labels0

2

0

0

labels1

2

0

0

16

h2mg_2_70

2048

2

labels0

2

0

0

labels1

2

0

0

17

h2mg_2_80

2048

2

labels0

2

0

0

labels1

2

0

0.01

18

h2mg_2_90

2048

2

labels0

2

0

0

labels1

2

0

0.02

19

h2mg_4_10

2048

4

labels0

2

0

0

labels1

2

0

0

20

h2mg_4_20

2048

4

labels0

2

0

0

labels1

2

0

0.01

21

h2mg_4_30

2048

4

labels0

2

0

0

labels1

2

0

0.01

22

h2mg_4_40

2048

4

labels0

2

0

0

labels1

2

0

0.01

23

h2mg_4_50

2048

4

labels0

2

0

0

labels1

2

0

0

24

h2mg_4_60

2048

4

labels0

2

0

0

labels1

2

0

0.02

25

h2mg_4_70

2048

4

labels0

2

0

0

labels1

2

0

0.02

26

h2mg_4_80

2048

4

labels0

2

0

0

labels1

2

0

0.02

27

h2mg_4_90

2048

4

labels0

2

0

0

labels1

2

0

0.02

28

h2mg_8_10

2048

8

labels0

2

0

0

labels1

2

0

0

29

h2mg_8_20

2048

8

labels0

2

0

0

labels1

2

0

0

30

h2mg_8_30

2048

8

labels0

2

0

0

labels1

2

0

0.02

31

h2mg_8_40

2048

8

labels0

2

0

0

labels1

2

0

0.02

32

h2mg_8_50

2048

8

labels0

2

0

0

labels1

2

0

0

33

h2mg_8_60

2048

8

labels0

2

0

0

labels1

2

0

0

34

h2mg_8_70

2048

8

labels0

2

0

0

labels1

2

0

0

35

h2mg_8_80

2048

8

labels0

2

0

0

labels1

2

0

0.01

36

h2mg_8_90

2048

8

labels0

2

0

0

labels1

2

0

0.01

37

h2mg_16_10

2048

16

labels0

2

0

0

labels1

2

0

0

38

h2mg_16_20

2048

16

labels0

2

0

0

labels1

2

0

0

39

h2mg_16_30

2048

16

labels0

2

0

0

labels1

2

0

0

40

h2mg_16_40

2048

16

labels0

2

0

0

labels1

2

0

0.01

41

h2mg_16_50

2048

16

labels0

2

0

0

labels1

2

0

0.02

42

h2mg_16_60

2048

16

labels0

2

0

0

labels1

2

0

0

43

h2mg_16_70

2048

16

labels0

2

0

0

labels1

2

0

0.01

44

h2mg_16_80

2048

16

labels0

2

0

0

labels1

2

0

0

45

h2mg_16_90

2048

16

labels0

2

0

0

labels1

2

0

0

46

h2mg_32_10

2048

32

labels0

2

0

0

labels1

2

0

0

47

h2mg_32_20

2048

32

labels0

2

0

0

labels1

2

0

0.01

48

h2mg_32_30

2048

32

labels0

2

0

0

labels1

2

0

0.01

49

h2mg_32_40

2048

32

labels0

2

0

0

labels1

2

0

0.02

50

h2mg_32_50

2048

32

labels0

2

0

0

labels1

2

0

0.02

51

h2mg_32_60

2048

32

labels0

2

0

0

labels1

2

0

0.01

52

h2mg_32_70

2048

32

labels0

2

0

0

labels1

2

0

0.01

53

h2mg_32_80

2048

32

labels0

2

0

0

labels1

2

0

0

54

h2mg_32_90

2048

32

labels0

2

0

0

labels1

2

0

0.01

55

h2mg_64_10

2048

64

labels0

2

0

0

labels1

2

0

0

56

h2mg_64_20

2048

64

labels0

2

0

0

labels1

2

0

0

57

h2mg_64_30

2048

64

labels0

2

0

0

labels1

2

0

0.01

58

h2mg_64_40

2048

64

labels0

2

0

0

labels1

2

0

0.01

59

h2mg_64_50

2048

64

labels0

2

0

0

labels1

2

0

0

60

h2mg_64_60

2048

64

labels0

2

0

0

labels1

2

0

0.01

61

h2mg_64_70

2048

64

labels0

2

0

0

labels1

2

0

0.01

62

h2mg_64_80

2048

64

labels0

2

0

0

labels1

2

0

0

63

h2mg_64_90

2048

64

labels0

2

0

0

labels1

2

0

0

64

h2mg_128_10

2048

128

labels0

2

0

0

labels1

2

0

0

65

h2mg_128_20

2048

128

labels0

2

0

0

labels1

2

0

0.01

66

h2mg_128_30

2048

128

labels0

2

0

0

labels1

2

0

0

67

h2mg_128_40

2048

128

labels0

2

0

0

labels1

2

0

0.01

68

h2mg_128_50

2048

128

labels0

2

0

0

labels1

2

0

0.01

69

h2mg_128_60

2048

128

labels0

2

0

0

labels1

2

0

0.01

70

h2mg_128_70

2048

128

labels0

2

0

0

labels1

2

0

0.01

71

h2mg_128_80

2048

128

labels0

2

0

0

labels1

2

0

0.01

72

h2mg_128_90

2048

128

labels0

2

0

0

labels1

2

0

0.01