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3.02k
4.03k
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15 classes
0araucaria_excelsa
0araucaria_excelsa
0araucaria_excelsa
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1chinese_fan_palm
1chinese_fan_palm
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1chinese_fan_palm
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1chinese_fan_palm
1chinese_fan_palm
1chinese_fan_palm
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2chinese_hackberry
2chinese_hackberry
2chinese_hackberry
2chinese_hackberry
2chinese_hackberry
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2chinese_hackberry
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2chinese_hackberry
3chinese_tallow_tree
3chinese_tallow_tree
3chinese_tallow_tree
3chinese_tallow_tree
3chinese_tallow_tree
3chinese_tallow_tree
3chinese_tallow_tree
3chinese_tallow_tree
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3chinese_tallow_tree
3chinese_tallow_tree
3chinese_tallow_tree
3chinese_tallow_tree
3chinese_tallow_tree
4comphor_tree
4comphor_tree
4comphor_tree
4comphor_tree
4comphor_tree
4comphor_tree
4comphor_tree
4comphor_tree
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
5formosan_sweet_gum
6honduras_mahogany
6honduras_mahogany
6honduras_mahogany
6honduras_mahogany
6honduras_mahogany
6honduras_mahogany
6honduras_mahogany
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6honduras_mahogany
6honduras_mahogany
6honduras_mahogany
7hoop_pine
7hoop_pine
7hoop_pine
7hoop_pine

Dataset Card for "NTU-Stem"

sample

The NTU Tree Dataset is a high-resolution few-shot learning dataset of the stem images of 15 different tree species found in the National Taiwan University (NTU) campus. The dataset was collected using personal cellphones in an effort to increase familiarity with the campus’s natural beauty.

The dataset includes images of the stem of the following 15 tree species, along with their Chinese and English names:

中文名稱 English Name
大王椰子 Royal Palm
土肉桂 Indigenous Cinnamon Tree
大葉桃花心木 Honduras Mahogany
小葉南洋杉 Araucaria Excelsa
石栗 Indian Walnut
朴樹 Chinese Hackberry
血桐 Macaranga
垂榕 White Barkfig
肯氏南洋杉 Hoop Pine
美人樹 Floss-silk Tree
烏桕 Chinese Tallow Tree
楓香 Formosan Sweet Gum
榕樹 Marabutan
蒲葵 Chinese Fan Palm
樟樹 Comphor Tree

The dataset contains a total of 240 images, with each species class containing 8 to 16 training images and the remaining 8 to 10 images serving as test data. The images were captured at a resolution of approximately 3k x 3k pixels, providing high detail for the purpose of few-shot learning.

Acknowledgements

This dataset was collected by the following students of National Taiwan University in Graduate Institute of Networking and Multimedia (GINM) and the Department of Computer Science and Information Engineering (CSIE).

Thanks to @liswei, @roger0426, @CYLiao1127, and @j1u2l3i4a5n for collecting the dataset.

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