csgo-object-detection

<div align="center">
<img width="640" alt="keremberke/csgo-object-detection" src="https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/thumbnail.jpg">
</div>

### Dataset Labels

```
['ct', 'cthead', 't', 'thead']
```

### Number of Images

```json
{'train': 3879, 'valid': 383, 'test': 192}
```

### How to Use

- Install [datasets](https://pypi.org/project/datasets/):

```bash
pip install datasets
```

- Load the dataset:

```python
from datasets import load_dataset

ds = load_dataset("keremberke/csgo-object-detection", name="full")
example = ds['train'][0]
```

### Roboflow Dataset Page
[https://universe.roboflow.com/asd-culfr/wlots/dataset/1](https://universe.roboflow.com/asd-culfr/wlots/dataset/1?ref=roboflow2huggingface)

### Citation

```
@misc{ wlots_dataset,
title = { wlots Dataset },
type = { Open Source Dataset },
author = { asd },
howpublished = { \\url{ https://universe.roboflow.com/asd-culfr/wlots } },
url = { https://universe.roboflow.com/asd-culfr/wlots },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { may },
note = { visited on 2023-01-27 },
}
```

### License
CC BY 4.0

### Dataset Summary
This dataset was exported via roboflow.com on December 28, 2022 at 8:08 PM GMT

Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time

It includes 4454 images.
Ct-cthead-t-thead are annotated in COCO format.

The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 416x416 (Fill (with center crop))

The following augmentation was applied to create 3 versions of each source image:
* Random brigthness adjustment of between -15 and +15 percent

<div align="center">
<img width="640" alt="keremberke/csgo-object-detection" src="https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/thumbnail.jpg">
</div>

### 数据集标签

['反恐精英（ct）', '反恐精英头部（cthead）', '恐怖分子（t）', '恐怖分子头部（thead）']

### 图像数量

json
{'训练集': 3879, '验证集': 383, '测试集': 192}

### 使用方法

- 安装[数据集（datasets）](https://pypi.org/project/datasets/):

bash
pip install datasets

- 加载数据集:

python
from datasets import load_dataset

ds = load_dataset("keremberke/csgo-object-detection", name="full")
example = ds['train'][0]

### Roboflow 数据集页面
[https://universe.roboflow.com/asd-culfr/wlots/dataset/1](https://universe.roboflow.com/asd-culfr/wlots/dataset/1?ref=roboflow2huggingface)

### 引用格式

@misc{ wlots数据集,
title = { wlots 数据集 },
type = { 开源数据集 },
author = { asd },
howpublished = { url{ https://universe.roboflow.com/asd-culfr/wlots } },
url = { https://universe.roboflow.com/asd-culfr/wlots },
journal = { Roboflow 开源社区 },
publisher = { Roboflow },
year = { 2022 },
month = { 5月 },
note = { 2023年1月27日访问 }
}

### 许可证
CC BY 4.0 许可协议

### 数据集概述
本数据集于2022年12月28日格林威治标准时间20:08通过roboflow.com导出。

Roboflow是一款端到端的计算机视觉平台，可助力用户完成以下任务：
* 与团队协同开展计算机视觉项目
* 收集并整理图像数据
* 解析非结构化图像数据
* 进行图像标注并构建数据集
* 导出、训练并部署计算机视觉模型
* 使用主动学习方法随时间迭代优化数据集

该数据集共包含4454张图像，其中ct、cthead、t、thead四类目标均采用COCO格式进行标注。

已对每张图像执行以下预处理操作：
* 像素数据自动校正（移除EXIF方向信息）
* 调整图像尺寸至416×416（采用居中裁剪填充方式）

以下数据增强操作被应用于每张原始图像以生成3个变种版本：
* 将图像亮度随机调整±15%