semvec/pypi-packages huggingface dataset:
paquet-basique 0.1
Un package pour créer une app Django avec la structure de base
License: MIT
Requires Python: >=3.10

# Paquet Basique Django

Ce projet est un package qui vous permet d'avoir une structure basique afin de créer une application Django.

## Prérequis
- Python 3.10 ou plus
- Django (installez avec `pip install django`)

## Installation

1. Activez l'environnement virtuel :  
   - Sous Linux/Mac :  
     ```dans le bash
     source venv/bin/activate
     ```
   - Sous Windows :  
     ```dans le powershell
     venv\Scripts\activate
     ```

2. Installez les dépendances :  
   ```dans le bash
   pip install -r requirements.txt
   
Utilisation
- Pour lancer le serveur de développement, exécutez : 'python manage.py runserver'
- Pour créer une nouvelle application Django : 'python manage.py startapp exemple_app'
SVMODEL 0.1.0
A simple CNN wrapper with training, evaluation, and plotting utilities and Simple powerful object detection and auto labeling , secure your model with the password
License: MIT
Requires Python: >=3.7

# 🧠 SmartModelCNN

> A unified deep learning library for image **classification**, **object detection**, and **automatic dataset labeling** — powered by TensorFlow, OpenCV, and NumPy — but easy to use with **just one line of code**!

[![PyPI version](https://img.shields.io/pypi/v/SmartModelVision)](https://pypi.org/project/SmartModelVision/)
[![Python](https://img.shields.io/pypi/pyversions/SmartModelVision)](https://pypi.org/project/SmartModelVision/)
[![License](https://img.shields.io/github/license/YKandoloJean/SMARTVISIONCNN)](https://github.com/YKandoloJean/SMARTVISIONCNN)

---

## 🔥 Features

### 🎯 Object Detection — `SVOL` (Smart Vision Object Locator)
- 🚀 Pre-trained EfficientDet D0 model from TensorFlow Hub
- 🖼️ Automatic image preprocessing, bounding box detection, and drawing
- 📹 Real-time detection from webcam feed
- 🎯 High accuracy object localization on COCO dataset classes

### 🧠 Image Classification — `SmartVisionCNN`
- 🧱 Customizable convolutional neural network architecture
- 🧪 Supports training, evaluation, and accuracy/loss visualization
- 💾 Save and load trained models seamlessly
- 🔄 Easily add custom layers like Dropout for regularization

### 📝 Automatic Dataset Labeling — `AutoLabeler`
- 🤖 Automatically generate bounding box annotations for unlabeled image datasets
- 📁 Supports saving annotations in YOLO `.txt` and Pascal VOC `.xml` formats
- 🔍 Uses SVOL detection results for labeling with configurable confidence threshold
- 🎯 Requires user to provide class labels for accurate annotation generation

### 🔒 Model Protection — `ModelProtector`
- 🔐 Password-protect your trained models to restrict unauthorized access
- 🔓 Unlock models via password prompt to enable predictions and saving
- 🔒 Simple and secure file-based locking mechanism
- 🛡️ Prevents accidental or malicious model usage without permission

---

## 📦 Installation

Install the latest release from PyPI:

```bash
pip install SVMODEL