README.md

---
license: cc-by-4.0
task_categories:
- image-classification
pretty_name: Lensless Freshwater Plankton Dataset
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': ACTINOSPHAERIUM NUCLEOFILUM
          '1': ARCELLA VULGARIS
          '2': BLEPHARISMA AMERICANUM
          '3': DIDINIUM NASUTUM
          '4': DILEPTUS
          '5': EUPLOTES EURYSTOMUS
          '6': PARAMECIUM  BURSARIA
          '7': SPIROSTOMUM AMBIGUUM
          '8': STENTOR COERULEUS
          '9': VOLVOX
  splits:
  - name: train
    num_bytes: 27638352.0
    num_examples: 5000
  - name: test
    num_bytes: 7603308.0
    num_examples: 1400
  download_size: 31826729
  dataset_size: 35241660.0
  description: A mixture of ten freshwater plankton species imaged with a lensless
    microscope designed for in situ data collection.
  dataset_name: Lensless Freshwater Plankton Dataset
  citation: "@article{dataset:lensless,\n  title     = {Annotation-free learning of\
    \ plankton for classification and anomaly detection},\n  author    = {Pastore,\
    \ V.P. and Zimmerman, T.G. and Biswas, S.K. and Bianco, S.},\n  journal   = {Scientific\
    \ Reports},\n  volume    = {10},\n  number    = {1},\n  pages     = {12142},\n\
    \  year      = {2020},\n  publisher = {Nature Publishing Group},\n  doi      \
    \ = {10.1038/s41598-020-68662-3},\n  url       = {https://doi.org/10.1038/s41598-020-68662-3}\n\
    }"
  homepage: https://ibm.ent.box.com/v/PlanktonData
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
dataset_description: A mixture of ten freshwater plankton species imaged with a lensless
  microscope designed for in situ data collection.
citation_bibtex: "@article{dataset:lensless,\n  title     = {Annotation-free learning\
  \ of plankton for classification and anomaly detection},\n  author    = {Pastore,\
  \ V.P. and Zimmerman, T.G. and Biswas, S.K. and Bianco, S.},\n  journal   = {Scientific\
  \ Reports},\n  volume    = {10},\n  number    = {1},\n  pages     = {12142},\n \
  \ year      = {2020},\n  publisher = {Nature Publishing Group},\n  doi       = {10.1038/s41598-020-68662-3},\n\
  \  url       = {https://doi.org/10.1038/s41598-020-68662-3}\n}"
citation_apa: "Pastore, V. P., Zimmerman, T. G., Biswas, S. K., & Bianco, S. (2020).\
  \ Annotation-free learning of \nplankton for classification and anomaly detection.\
  \ Scientific Reports, 10(1), 12142. https://doi.org/10.1038/s41598-020-68662-3\n"
hgfc_dataset_name: lensless
source_url: https://ibm.ent.box.com/v/PlanktonData
hf_dataset_name: lensless
hf_org_name: project-oceania
report_markdown: "**Samples per class for split `train`**\n ```────────────────────────\
  \ Label histogram for train split  ─────────────────────────\n0: ACTINOSPHAERIUM\
  \ NUCLEOFILUM ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00\n1: ARCELLA\
  \ VULGARIS            ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00\n2:\
  \ BLEPHARISMA AMERICANUM      ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00\n\
  3: DIDINIUM NASUTUM            ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00\n\
  4: DILEPTUS                    ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00\n\
  5: EUPLOTES EURYSTOMUS         ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00\n\
  6: PARAMECIUM  BURSARIA        ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00\n\
  7: SPIROSTOMUM AMBIGUUM        ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00\n\
  8: STENTOR COERULEUS           ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00\n\
  9: VOLVOX                      ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00\n\
  ```\n\n**Samples per class for split `test`**\n ```───────────────────────── Label\
  \ histogram for test split  ─────────────────────────\n0: ACTINOSPHAERIUM NUCLEOFILUM\
  \ ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00\n1: ARCELLA VULGARIS   \
  \         ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00\n2: BLEPHARISMA\
  \ AMERICANUM      ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00\n3: DIDINIUM\
  \ NASUTUM            ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00\n4: DILEPTUS\
  \                    ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00\n5: EUPLOTES\
  \ EURYSTOMUS         ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00\n6: PARAMECIUM\
  \  BURSARIA        ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00\n7: SPIROSTOMUM\
  \ AMBIGUUM        ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00\n8: STENTOR\
  \ COERULEUS           ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00\n9:\
  \ VOLVOX                      ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00\n\
  ```\n"
dataset_means: '[0.4140324021223468, 0.43283753018640264, 0.4201652068819248]'
dataset_stds: '[0.12368459767878866, 0.1250351586616661, 0.13132434456183137]'
---
# Dataset *Lensless Freshwater Plankton Dataset*
A mixture of ten freshwater plankton species imaged with a lensless microscope designed for in situ data collection.

- **Original dataset available online at:**  <https://ibm.ent.box.com/v/PlanktonData>.
- **Original dataset license:** <cc-by-4.0>.

## Details

- **train split means (RGB):** [0.4140324021223468, 0.43283753018640264, 0.4201652068819248]
- **train split standard deviations (RGB):** [0.12368459767878866, 0.1250351586616661, 0.13132434456183137]

**Samples per class for split `train`**
 ```──────────────────────── Label histogram for train split  ─────────────────────────
0: ACTINOSPHAERIUM NUCLEOFILUM ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00
1: ARCELLA VULGARIS            ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00
2: BLEPHARISMA AMERICANUM      ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00
3: DIDINIUM NASUTUM            ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00
4: DILEPTUS                    ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00
5: EUPLOTES EURYSTOMUS         ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00
6: PARAMECIUM  BURSARIA        ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00
7: SPIROSTOMUM AMBIGUUM        ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00
8: STENTOR COERULEUS           ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00
9: VOLVOX                      ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 500.00
```

**Samples per class for split `test`**
 ```───────────────────────── Label histogram for test split  ─────────────────────────
0: ACTINOSPHAERIUM NUCLEOFILUM ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00
1: ARCELLA VULGARIS            ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00
2: BLEPHARISMA AMERICANUM      ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00
3: DIDINIUM NASUTUM            ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00
4: DILEPTUS                    ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00
5: EUPLOTES EURYSTOMUS         ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00
6: PARAMECIUM  BURSARIA        ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00
7: SPIROSTOMUM AMBIGUUM        ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00
8: STENTOR COERULEUS           ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00
9: VOLVOX                      ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 140.00
```


## Reference
Pastore, V. P., Zimmerman, T. G., Biswas, S. K., & Bianco, S. (2020). Annotation-free learning of 
plankton for classification and anomaly detection. Scientific Reports, 10(1), 12142. https://doi.org/10.1038/s41598-020-68662-3


### BibTEX
```bibtex
@article{dataset:lensless,
  title     = {Annotation-free learning of plankton for classification and anomaly detection},
  author    = {Pastore, V.P. and Zimmerman, T.G. and Biswas, S.K. and Bianco, S.},
  journal   = {Scientific Reports},
  volume    = {10},
  number    = {1},
  pages     = {12142},
  year      = {2020},
  publisher = {Nature Publishing Group},
  doi       = {10.1038/s41598-020-68662-3},
  url       = {https://doi.org/10.1038/s41598-020-68662-3}
}
```

## Usage
```python
from datasets import load_dataset

dataset = load_dataset("project-oceania/lensless")
```