semvec/pypi-packages huggingface dataset:
vtscan 1.0.20
Utility to scan for malicious files using the VirusTotal API
License: MIT
Requires Python: >=3.4

![pypi](https://img.shields.io/pypi/v/vtscan.svg)
![python](https://img.shields.io/pypi/pyversions/vtscan.svg)
![docs](https://readthedocs.org/projects/vtscan/badge/?version=latest)
![license](https://img.shields.io/github/license/prahladyeri/vtscan.svg)
![last-commit](https://img.shields.io/github/last-commit/prahladyeri/vtscan.svg)
[![patreon](https://img.shields.io/badge/Patreon-brown.svg?logo=patreon)](https://www.patreon.com/prahladyeri)
[![paypal](https://img.shields.io/badge/PayPal-blue.svg?logo=paypal)](https://paypal.me/prahladyeri)
[![follow](https://img.shields.io/twitter/follow/prahladyeri.svg?style=social)](https://twitter.com/prahladyeri)

# vtscan

Command line tool to scan for malicious files using the VirusTotal API

# Installation

	pip install vtscan

# Usage

```bash
C:\> vtscan c:\programs\FileZilla-3.44.0\filezilla.exe
calculating sha1 hash...
done. sending scan request...

Found in VT Database
permalink:  https://www.virustotal.com/gui/file/9bbf15a489e7e109f8e238013846a29448c7994a46b7507be239a2aeeccf99f7/detection/f-9bbf15a489e7e109f8e238013846a29448c7994a46b7507be239a2aeeccf99f7-1656398328
Number of positives: 0 (out of 66 scanners applied)
verbose_msg: Scan finished, information embedded
7a37556cc8d665b508dd118fb3baec27b7891fb1 is not malicious

done

C:\>
```

# Notes

You'll need a VirusTotal API key to use this program, you can get one by registering a free account at [www.virustotal.com](https://www.virustotal.com). Once you have the API key, you can just put it in the `%userprofile%\.config\vtscan-settings.json` directory.


## Donation

Please consider donating if this tool has helped you in any way.

- [Donate through PayPal](https://www.paypal.me/prahladyeri)
- [Donate through Patreon](https://www.patreon.com/prahladyeri)

You can also hire me through [Upwork](https://www.upwork.com/freelancers/~01e977ff45b62e031c) or [Fiverr](https://www.fiverr.com/prahladyeri) or [contact me directly](mailto:prahladyeri@yahoo.com) to get professional support and customization.
yolo-convert 0.1.6
YOLOv5 model conversion CLI
License: MIT

## 上传步骤
在你的项目根目录（pyproject.toml 所在目录）执行：
```
python -m build
```
- 这个命令会生成一个 dist/ 文件夹。
- 文件通常是:
  - `yolo_convert-0.1.0-py3-none-any.whl`
  - `yolo_convert-0.1.0.tar.gz`

再上传包:
```
twine upload dist/*
```

## 本地开发
```
pip uninstall yolo-convert      # 卸载 PyPI 版本，避免冲突
pip install -e .                # 安装本地 editable 版本
yolo_convert -i yolov5s.pt
```
## 安装测试
安装python环境

```bash
pip install yolo-convert
```

### 导出Yolov5模型，一个输出
默认分辨率640x640、opset版本12

```bash
yolo_convert -i yolov5.pt
```

修改分辨率为1024x1280(h,w)，opset设置13

```bash
yolo_convert -i yolov5.pt --img 1024 1280 --opset 13
```

动态分辨率

```bash
yolo_convert -i yolov5.pt --dynamic 
```

### 导出Yolov5模型，三个输出
固定分辨率

```bash
yolo_convert -i yolov5.pt --hisi3559 --img 1024 1280
```
动态分辨率

```bash
yolo_convert -i yolov5.pt --hisi3559 --dynamic
```

## Atlas

Atlas动态分辨率+后处理算子导出，`--class-num`指定类别个数，`--biases`默认不变，如果改变需要重新传anchor,第一个数为float型(带点，如10.0)

```bash
yolo_convert -i yolov5.pt --atlas --dynamic --class-num 3 --biases 10.0 13 16 30 33 23 30 61 62 45 59 119 116 90 156 198 373 326
```

如果训练后的anchor保持不变，可简化为：

```bash
yolo_convert -i yolov5.pt --atlas --dynamic --class-num 3
```

## 3403
3403后处理算子导出,只支持固定分辨率，加--dynaic会报错，只加入--img h w

```bash
yolo_convert -i yolov5.pt --hisi3403 --img 1024 1280
```

## 3519
3519后处理算子导出,同3403，只支持固定分辨率，加--dynaic会报错，只加入--img h w

```bash
yolo_convert -i yolov5.pt --hisi3519 --img 1024 1280

```

## Tensorrt efficientNms
Tensorrt导出带efficientNms plugin,只支持动态分辨率

```bash
yolo_convert -i yolov5s.pt --tensorrt --dynamic
```
jobson-xhs-mcp 0.1.1
小红书MCP服务器 - 提供小红书API功能的MCP工具
License: MIT
Requires Python: >=3.12
Keywords: api, mcp, server, xiaohongshu

# 小红书MCP服务
[![smithery badge](https://smithery.ai/badge/@jobsonlook/xhs-mcp)](https://smithery.ai/server/@jobsonlook/xhs-mcp)
## 特点
- [x] 采用js逆向出x-s,x-t,直接请求http接口,无须笨重的playwright
- [x] 搜索笔记
- [x] 获取笔记内容
- [x] 获取笔记的评论
- [x] 发表评论

![特性](https://raw.githubusercontent.com/jobsonlook/xhs-mcp/master/docs/feature.png)

## 快速开始

### 1. 环境
 * node
 * python 3.12
 * uv (pip install uv)

### 2. 安装依赖
```sh

git clone git@github.com:jobsonlook/xhs-mcp.git

cd xhs-mcp
uv sync 

```

### 3. 获取小红书的cookie
[打开web小红书](https://www.xiaohongshu.com/explore)
登录后，获取cookie，将cookie配置到第4步的 XHS_COOKIE 环境变量中
![cookie](https://raw.githubusercontent.com/jobsonlook/xhs-mcp/master/docs/cookie.png)

### 4. 配置mcp server

```json
{
    "mcpServers": {
        "xhs-mcp": {
            "command": "uv",
            "args": [
                "--directory",
                "/Users/xxx/xhs-mcp",
                "run",
                "main.py"
            ],
            "env": {
                "XHS_COOKIE": "xxxx"
            }
        }
    }
}
```

## 免责声明
本项目仅用于学习交流，禁止用于其他用途，任何涉及商业盈利目的均不得使用，否则风险自负。