semvec/pypi-packages huggingface dataset:
tepe 0.7.5.2
A concise and unified toolkit for Training, Evalutation, Prediction and Export (TEPE)
Requires Python: >=3.8

## TEPE
A concise and unified toolkit for **T**raining, **E**valutation, **P**rediction and **E**xport (**TEPE**).

<div align="center">

<img src="docs/images/title.png" />

</div>

&#x1F31F; **TEPE特点：** 将深度学习各个部件的构建与参数配置统一成一个任务类，提供默认参数和默认实现。开发者只需要继承任务类，改动相关参数，就可快速训练、验证、预测、导出自己的模型。

The supported algorithms are as follows, 

- classification: 
    - [x] MobileNetv2  &#x1F44C;
    - [x] SimpleNet  &#x1F44C;
- detection:
    - [x] SSDLite-RFB  &#x1F44C;
    - [x] YOLOv5  &#x1F44C;
    - [x] YOLOX  &#x1F44C;
    - [x] NanoDet  &#x1F44C;
- segmentation: 
    - [x] MobileNet-DeepLabv3  &#x1F44C;
- keypoint
    - [x] ShuffleNet-heatmap  &#x1F44C;
- anomaly detection:
    - [x] CFA  &#x1F44C;
    - [x] RD  &#x1F44C;
- few-shot detection:
    - [x] Attention-RPN
- line
    - [x] ShuffleNet-line  &#x1F44C;
- tracking

## 快速安装

可以直接运行下列命令，本地conda环境配置和服务器docker使用详见：[环境安装](./docs/Installation.md)

```bash
## step1 克隆仓库
git clone git@10.106.249.57:SD/tepe.git
## step2 转到tepe目录下
cd tepe
## step3 安装相关依赖
pip install -r requirements.txt
## step4 将tepe安装到环境中
pip install -v -e .  # or "python setup.py develop"
```

## 快速使用

```python
from tepe import get_task

task = get_task('yolov5')
task.data_root = ""
task.num_classes = 2
...

# 训练
task.train()

# 评估
task.weights = ""  # 模型权重路径
task.eval()

# 导出
task.export()

# 预测
task.predict(source={img|fold|video}, view_img=False, save_img=True)
```

## 通过配置文件调用（推荐）
### 1. 创建配置文件

参考`configs/samples`目录下的配置文件，**复制/创建**一个自己需要的config.py文件，它既可以当作配置文件，也可以当作程序运行的入口。它里面包含一个任务配置类，这个类需要继承一个默认的配置类，默认配置类在`tepe/tasks/*/task.py`中实现。

以yolov5表计检测为例：

```python
## config.py
from tepe.tasks.yolov5 import YOLOv5Config

class TaskConfig(YOLOv5Config):
    def __init__(self):
        super(TaskConfig, self).__init__()
        self.task_name = 'v5_meter'

        # dataset config ------------------
        self.CLASS = ('meter',)  
        self.num_classes = 1
        self.data_root = ""  # 数据集路径

        # model config --------------------
        self.yolov5_type = 'n'
        self.input_size = 320

        # training config -----------------
        self.max_epoch = 50
```

### 2. 调用配置文件

有两种调用方式：

**方法一：使用命令行**

```bash
tepe {train eval, predict, export} [-h]
```

**方法二：代码中实例化配置类，直接调用接口**


```python
## 实例化方法一：
task = TaskConfig()
## 实例化方法二：
import tepe
task = tepe.get_task("config.py")
```


## 更新日志：

**[2023/03/28]** shufflenet-line来了！通过heatmap的方式进行线段检测

**[2023/02/27]** shufflenet-heatmap-v2来了！效果更好的关键点检测方法

**[2022/11/21]** yolov5训练时可以支持yolo/voc/coco三种结构的数据集

**[2022/10/24]** 新的分支：添加异物检测分支anomaly_detection

**[2022/09/22]** 新的功能：可以通过http请求来进行存图片,训练,导出,预测等操作

**[2022/09/14]** 实现了异常检测(anomaly detection)知识蒸馏算法rd的训练train和导出export

**[2022/09/06]** few-shot算法attention_rpn目前可以在tepe上跑predict，详见[传送门](./tepe/tasks/attention_rpn/README.txt)

**[2022/04/07]** 仓库建立

---

更多任务的相关介绍、数据准备教程可见：

- [图像分类](./docs/tutorials/%E5%9B%BE%E5%83%8F%E5%88%86%E7%B1%BB.md)
- [目标检测](./docs/tutorials/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B.md)
- [语义分割](./docs/tutorials/%E8%AF%AD%E4%B9%89%E5%88%86%E5%89%B2.md)
maskcovid 0.0.3
maskcovid is a PyPI tool that predicts the number of people infected by day and up to one month from the date Japan began its liberal mask-wearing policy in the current Covid-19 epidemic.
Requires Python: >=3.7

## maskcovid
maskcovid is a PyPI tool that predicts the number of people infected by day and up to one month from the date Japan began its liberal mask-wearing policy in the current Covid-19 epidemic.<br>

The goal of maskcovid is to determine if policies such as the liberalization of mask wearing are correct based on the increase or decrease in the number of infected people.<br>

Japan's policy of liberalizing the wearing of masks began on March 13, 2023.<br>

It is difficult to judge now (as of April 3, 2023) because only a little time has passed since the policy was initiated, but an increase in the number of infected people in the future could show that the policy was wrong.<br>

maskcovid scrapes the latest data from the following site over the Internet : https://covid19.mhlw.go.jp/public/opendata/newly_confirmed_cases_daily.csv<br>

## How to install maskcovid
```
$ pip install maskcovid
```

## How to run maskcovid

```
$ maskcovid
```
<img src="https://github.com/i-inose/maskcovid/blob/main/maskcovid.png?raw=true">


Written by Izuru Inose<br>
-At the Takefuji Lab-