Datasets:
Tasks:
Image Classification
Modalities:
Image
File size: 1,869 Bytes
3b1a29d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: tree-class
## Dataset Description
This dataset has been automatically processed by AutoTrain for project tree-class.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<265x190 RGB PIL image>",
"target": 10
},
{
"image": "<800x462 RGB PIL image>",
"target": 6
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Burls \u7bc0\u7624', 'Canker \u6f70\u760d', 'Co-dominant branches \u7b49\u52e2\u679d', 'Co-dominant stems \u7b49\u52e2\u5e79', 'Cracks or splits \u88c2\u7e2b\u6216\u88c2\u958b', 'Crooks or abrupt bends \u4e0d\u5e38\u898f\u5f4e\u66f2', 'Cross branches \u758a\u679d', 'Dead surface roots \u8868\u6839\u67af\u840e ', 'Deadwood \u67af\u6728', 'Decay or cavity \u8150\u721b\u6216\u6a39\u6d1e', 'Fungal fruiting bodies \u771f\u83cc\u5b50\u5be6\u9ad4', 'Galls \u816b\u7624 ', 'Girdling root \u7e8f\u7e5e\u6839 ', 'Heavy lateral limb \u91cd\u5074\u679d', 'Included bark \u5167\u593e\u6a39\u76ae', 'Parasitic or epiphytic plants \u5bc4\u751f\u6216\u9644\u751f\u690d\u7269', 'Pest and disease \u75c5\u87f2\u5bb3', 'Poor taper \u4e0d\u826f\u6f38\u5c16\u751f\u9577', 'Root-plate movement \u6839\u57fa\u79fb\u4f4d ', 'Sap flow \u6ef2\u6db2', 'Trunk girdling \u7e8f\u7e5e\u6a39\u5e79 ', 'Wounds or mechanical injury \u50b7\u75d5\u6216\u6a5f\u68b0\u7834\u640d'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 225 |
| valid | 67 |
|