Dataset ZooLake Plankton Dataset

Plankton images annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera.

• Original dataset available online at: https://opendata.eawag.ch/dataset/52b6ba86-5ecb-448c-8c01-eec7cb209dc7/resource/1cc785fa-36c2-447d-bb11-92ce1d1f3f2d/download/data.zip.
• Original dataset license: <cc-by-4.0>.

Details

• train split means (RGB): [0.05492783056776769, 0.050561395292361595, 0.04523793400099787]
• train split standard deviations (RGB): [0.1515838323231226, 0.14021859660251165, 0.12578012700158586]

Samples per class for split train

0: aphanizomenon         ▇▇▇▇ 164.00
1: asplanchna            ▇▇▇▇▇▇▇▇▇ 410.00
2: asterionella          ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 735.00
3: bosmina               ▇ 51.00
4: brachionus            ▇▇ 93.00
5: ceratium              ▇▇▇▇▇▇▇▇▇▇▇▇ 558.00
6: chaoborus              7.00
7: conochilus            ▇▇▇▇ 189.00
8: copepod_skins         ▇ 24.00
9: cyclops               ▇▇▇▇▇▇▇▇▇▇▇▇▇ 591.00
10: daphnia              ▇▇▇▇▇▇▇▇▇▇▇ 510.00
11: daphnia_skins        ▇ 39.00
12: diaphanosoma         ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 769.00
13: diatom_chain          12.00
14: dinobryon            ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 2366.00
15: dirt                 ▇▇ 91.00
16: eudiaptomus          ▇▇▇▇▇▇▇▇ 375.00
17: filament             ▇▇▇▇▇▇ 276.00
18: fish                 ▇▇▇ 155.00
19: fragilaria           ▇▇▇▇▇ 215.00
20: hydra                 15.00
21: kellicottia          ▇▇▇▇▇▇▇▇ 375.00
22: keratella_cochlearis ▇▇ 84.00
23: keratella_quadrata   ▇▇▇▇▇▇ 285.00
24: leptodora            ▇▇▇ 144.00
25: maybe_cyano          ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 958.00
26: nauplius             ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 1044.00
27: paradileptus         ▇▇▇▇▇▇ 291.00
28: polyarthra           ▇ 57.00
29: rotifers             ▇▇▇▇▇▇▇▇▇▇▇▇ 535.00
30: synchaeta            ▇▇ 90.00
31: trichocerca          ▇▇▇▇ 174.00
32: unknown              ▇▇▇▇ 176.00
33: unknown_plankton     ▇ 49.00
34: uroglena             ▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 652.00

Samples per class for split validation

0: aphanizomenon         ▇▇▇ 33.00
1: asplanchna            ▇▇▇▇▇▇▇▇▇▇▇ 103.00
2: asterionella          ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 154.00
3: bosmina               ▇▇ 15.00
4: brachionus            ▇▇ 23.00
5: ceratium              ▇▇▇▇▇▇▇▇▇▇▇▇▇ 126.00
6: chaoborus              1.00
7: conochilus            ▇▇▇ 33.00
8: copepod_skins          3.00
9: cyclops               ▇▇▇▇▇▇▇▇▇▇▇▇▇ 127.00
10: daphnia              ▇▇▇▇▇▇▇▇▇▇ 99.00
11: daphnia_skins         2.00
12: diaphanosoma         ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 159.00
13: diatom_chain          2.00
14: dinobryon            ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 510.00
15: dirt                 ▇▇ 18.00
16: eudiaptomus          ▇▇▇▇▇▇▇▇▇ 87.00
17: filament             ▇▇▇▇▇▇▇ 64.00
18: fish                 ▇▇▇ 29.00
19: fragilaria           ▇▇▇▇▇▇ 56.00
20: hydra                 2.00
21: kellicottia          ▇▇▇▇▇▇▇▇ 79.00
22: keratella_cochlearis ▇▇ 15.00
23: keratella_quadrata   ▇▇▇▇▇▇▇ 70.00
24: leptodora            ▇▇▇ 26.00
25: maybe_cyano          ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 202.00
26: nauplius             ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 222.00
27: paradileptus         ▇▇▇▇▇▇▇ 72.00
28: polyarthra           ▇ 10.00
29: rotifers             ▇▇▇▇▇▇▇▇▇▇ 100.00
30: synchaeta            ▇▇ 23.00
31: trichocerca          ▇▇▇▇ 44.00
32: unknown              ▇▇▇ 26.00
33: unknown_plankton     ▇ 11.00
34: uroglena             ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 145.00

Samples per class for split test

0: aphanizomenon         ▇▇▇ 28.00
1: asplanchna            ▇▇▇▇▇▇▇▇▇▇▇ 93.00
2: asterionella          ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 165.00
3: bosmina               ▇▇ 14.00
4: brachionus            ▇▇ 21.00
5: ceratium              ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 130.00
6: chaoborus              2.00
7: conochilus            ▇▇▇▇▇ 42.00
8: copepod_skins         ▇ 6.00
9: cyclops               ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 148.00
10: daphnia              ▇▇▇▇▇▇▇▇▇▇▇▇▇ 112.00
11: daphnia_skins        ▇ 5.00
12: diaphanosoma         ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 161.00
13: diatom_chain          3.00
14: dinobryon            ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 446.00
15: dirt                 ▇▇▇ 22.00
16: eudiaptomus          ▇▇▇▇▇▇▇▇▇ 75.00
17: filament             ▇▇▇▇▇▇▇▇ 65.00
18: fish                 ▇▇▇▇ 38.00
19: fragilaria           ▇▇▇▇ 35.00
20: hydra                 1.00
21: kellicottia          ▇▇▇▇▇▇▇▇ 65.00
22: keratella_cochlearis ▇▇ 13.00
23: keratella_quadrata   ▇▇▇▇▇▇▇▇ 65.00
24: leptodora            ▇▇▇▇ 33.00
25: maybe_cyano          ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 204.00
26: nauplius             ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 241.00
27: paradileptus         ▇▇▇▇▇▇▇ 61.00
28: polyarthra           ▇ 12.00
29: rotifers             ▇▇▇▇▇▇▇▇▇▇▇▇▇ 110.00
30: synchaeta            ▇▇▇ 29.00
31: trichocerca          ▇▇▇▇ 37.00
32: unknown              ▇▇▇▇▇ 43.00
33: unknown_plankton     ▇ 11.00
34: uroglena             ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 156.00

Reference

Kyathanahally, S. P., Hardeman, T., Merz, E., Bulas, T., Reyes, M., Isles, P., Pomati, F., & Baity-Jesi, M. (2021). Deep learning classification of lake zooplankton. Frontiers in Microbiology, 12. https://doi.org/10.3389/fmicb.2021.746297

BibTEX

@article{dataset:zoolake,
  title   = {Deep learning classification of lake zooplankton},
  author  = {Kyathanahally, S.P. and Hardeman, T. and Merz, E. and Bulas, T. and Reyes, M. and Isles, P. and Pomati, F. and Baity-Jesi, M.},
  journal = {Frontiers in Microbiology},
  volume  = {12},
  year    = {2021},
  doi     = {10.3389/fmicb.2021.746297},
  url     = {https://www.frontiersin.org/articles/10.3389/fmicb.2021.746297}
}

Usage

from datasets import load_dataset

dataset = load_dataset("project-oceania/zoolake")