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10 Monkey Species
许可协议: CC-BY-SA 4.0

Overview

Content

The dataset consists of two files, training and validation. Each folder contains 10 subforders labeled as n0~n9, each corresponding a species form Wikipedia's monkey cladogram. Images are 400x300 px or larger and JPEG format (almost 1400 images). Images were downloaded with help of the googliser open source code.

Label mapping:
n0, alouatta_palliata
n1, erythrocebus_patas
n2, cacajao_calvus 
n3, macaca_fuscata   
n4, cebuella_pygmea
n5, cebus_capucinus
n6, mico_argentatus
n7, saimiri_sciureus 
n8, aotus_nigriceps
n9, trachypithecus_johnii
  • For more information on the monkey species and number of images per class make sure to check monkey_labels.txt file.

Aim

This dataset is intended as a test case for fine-grain classification tasks, perhaps best used in combination with transfer learning. Hopefully someone can help us expand the number of classes or number of images.

Acknowledgements

Thanks to Romain Renard for his help with the code implementation. Also, thanks to Gustavo Montoya, Jacky Zhang and Sofia Loaiciga for their help with the dataset curation.

Notes

Some demo code for usage of the dataset in combination with Keras can be found in this repo.

数据概要
数据格式
image,
数据量
1.371K
文件大小
547.05GB
发布方
Mario
| 数据量 1.371K | 大小 547.05GB
10 Monkey Species
许可协议: CC-BY-SA 4.0

Overview

Content

The dataset consists of two files, training and validation. Each folder contains 10 subforders labeled as n0~n9, each corresponding a species form Wikipedia's monkey cladogram. Images are 400x300 px or larger and JPEG format (almost 1400 images). Images were downloaded with help of the googliser open source code.

Label mapping:
n0, alouatta_palliata
n1, erythrocebus_patas
n2, cacajao_calvus 
n3, macaca_fuscata   
n4, cebuella_pygmea
n5, cebus_capucinus
n6, mico_argentatus
n7, saimiri_sciureus 
n8, aotus_nigriceps
n9, trachypithecus_johnii
  • For more information on the monkey species and number of images per class make sure to check monkey_labels.txt file.

Aim

This dataset is intended as a test case for fine-grain classification tasks, perhaps best used in combination with transfer learning. Hopefully someone can help us expand the number of classes or number of images.

Acknowledgements

Thanks to Romain Renard for his help with the code implementation. Also, thanks to Gustavo Montoya, Jacky Zhang and Sofia Loaiciga for their help with the dataset curation.

Notes

Some demo code for usage of the dataset in combination with Keras can be found in this repo.

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