minhqngo/tiny-imagenet

---
annotations_creators:
- crowdsourced
extra_gated_prompt: "By clicking on \u201CAccess repository\u201D below, you also\
\ agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\"\
) has requested permission to use the ImageNet database (the \"Database\") at Princeton\
\ University and Stanford University. In exchange for such permission, Researcher\
\ hereby agrees to the following terms and conditions:\n1. Researcher shall use\
\ the Database only for non-commercial research and educational purposes.\n2. Princeton\
\ University, Stanford University and Hugging Face make no representations or warranties\
\ regarding the Database, including but not limited to warranties of non-infringement\
\ or fitness for a particular purpose.\n3. Researcher accepts full responsibility\
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\ employees, Trustees, officers and agents, against any and all claims arising from\
\ Researcher's use of the Database, including but not limited to Researcher's use\
\ of any copies of copyrighted images that he or she may create from the Database.\n\
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\ Database provided that they first agree to be bound by these terms and conditions.\n\
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7. The law of the State of New Jersey shall apply to all disputes under this agreement."
language:
- en
language_creators:
- crowdsourced
license: []
multilinguality:
- monolingual
paperswithcode_id: imagenet
pretty_name: Tiny-ImageNet
size_categories:
- 100K<n<1M
source_datasets:
- extended|imagenet-1k
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---

# Dataset Card for tiny-imagenet

## Dataset Description

- **Homepage:** https://www.kaggle.com/c/tiny-imagenet
- **Repository:** [Needs More Information]
- **Paper:** http://cs231n.stanford.edu/reports/2017/pdfs/930.pdf
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-tiny-imagenet-1

### Dataset Summary

Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images.

### Languages

The class labels in the dataset are in English.

## Dataset Structure

### Data Instances

```json
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190,
'label': 15
}
```

### Data Fields

- image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
- label: an int classification label. -1 for test set as the labels are missing. Check `classes.py` for the map of numbers & labels.

### Data Splits

| | Train | Valid |
| ------------ | ------ | ----- |
| # of samples | 100000 | 10000 |

## Usage

### Example

#### Load Dataset
```python
def example_usage():
tiny_imagenet = load_dataset('Maysee/tiny-imagenet', split='train')
print(tiny_imagenet[0])

if __name__ == '__main__':
example_usage()
```

---
标注创建者：
- 众包
额外授权提示："点击下方「访问仓库」按钮，即代表您同意遵守ImageNet使用条款(ImageNet Terms of Access)：
[研究人员全名]（下称「研究人员」）已申请使用普林斯顿大学与斯坦福大学维护的ImageNet数据库(ImageNet Database)。作为获取使用权限的交换条件，研究人员特此同意以下条款：
1. 研究人员仅可将数据库用于非商业性研究与教育目的。
2. 普林斯顿大学、斯坦福大学与Hugging Face不对数据库作出任何明示或默示担保，包括但不限于不侵权担保或特定用途适用性担保。
3. 研究人员需对其使用数据库的行为承担全部责任，并需就因使用数据库（包括但不限于研究人员从数据库生成的受版权保护的图像副本的使用行为）所引发的任何及所有索赔，对ImageNet团队、普林斯顿大学、斯坦福大学与Hugging Face及其雇员、理事、官员与代理人进行辩护并赔偿。
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7. 本协议项下的所有争议均适用新泽西州法律。"
语言：
- 英语
语言创建者：
- 众包
许可证：无
多语言属性：
- 单语言
PapersWithCode编号：imagenet
友好名称：Tiny-ImageNet
样本规模区间：10万<样本数<100万
源数据集：
- 扩展版|imagenet-1k
任务类别：
- 图像分类
任务子类型：
- 多类别图像分类
---

# 数据集卡片：Tiny-ImageNet

## 数据集描述
- **"主页"**：https://www.kaggle.com/c/tiny-imagenet
- **"仓库"**：[需补充更多信息]
- **"论文"**：http://cs231n.stanford.edu/reports/2017/pdfs/930.pdf
- **"排行榜"**：https://paperswithcode.com/sota/image-classification-on-tiny-imagenet-1

### 数据集摘要
Tiny ImageNet包含200个类别的100000张图像（每个类别500张），图像已被调整为64×64的彩色图像。每个类别包含500张训练图像、50张验证图像与50张测试图像。

### 语言说明
本数据集的类别标签采用英语。

## 数据集结构
### 数据实例
json
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190>,
'label': 15
}

### 数据字段
- image：包含图像的PIL.Image.Image对象。请注意，在访问图像列时：`dataset[0]"image"`会自动对图像文件进行解码。解码大量图像文件可能会耗费大量时间，因此建议优先通过样本索引查询图像列，即优先使用`dataset[0]"image"`而非`dataset["image"][0]`。
- label：整数类型的分类标签。测试集的标签缺失，此时label为-1。可查看`classes.py`文件获取数字标签与类别名称的映射关系。

### 数据划分
| | 训练集 | 验证集 |
| ------------ | ------ | ----- |
| 样本数量 | 100000 | 10000 |

## 使用方法
### 示例
#### 加载数据集
python
def example_usage():
tiny_imagenet = load_dataset('Maysee/tiny-imagenet', split='train')
print(tiny_imagenet[0])

if __name__ == '__main__':
example_usage()