bazudde/Sweetpotato_images

---
task_categories:
- image-classification

---
# AutoTrain Dataset for project: sweet-potato-classification

## Dataset Description

This dataset has been automatically processed by AutoTrain for project sweet-potato-classification.

### 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": "<256x192 RGB PIL image>",
"target": 0
},
{
"image": "<256x192 RGB PIL image>",
"target": 0
}
]
```

### Dataset Fields

The dataset has the following fields (also called "features"):

```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Leaf rust', 'Root rot', 'alternaria_sweet_potato_leaf_spot'], 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 | 46 |
| valid | 13 |

原始信息汇总

数据集概述

数据集名称

AutoTrain Dataset for project: sweet-potato-classification

任务类别

• 图像分类

语言

• 语言代码：unk

数据集结构

数据实例

• 示例数据包含图像和目标标签。
• 图像为256x192 RGB PIL图像。
• 目标标签为类别编号。

数据集字段

• 图像：Image(decode=True, id=None)
• 目标：ClassLabel(names=[Leaf rust, Root rot, alternaria_sweet_potato_leaf_spot], id=None)

数据集分割

• 训练集：46个样本
• 验证集：13个样本