# C-CNN

**Repository Path**: cz2006/C-CNN

## Basic Information

- **Project Name**: C-CNN
- **Description**: 一个跨平台的深度学习神经网络模型(卷积神经网络)，纯C语言实现，可以在windows、linux、android、stm32等嵌入式系统上面部署。
- **Primary Language**: C
- **License**: MIT
- **Default Branch**: master
- **Homepage**: http://www.1234998.cn
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 7
- **Created**: 2025-04-02
- **Last Updated**: 2025-04-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

This is a Convolutional Neural Network (CNN) in C. This project aims to showcase the power and versatility of the C language in building deep learning models. The Convolutional Neural Network is a popular and effective architecture for image classification, object detection, and other computer vision tasks. Through this project allowing users to train and deploy their own deep learning models.

Key features of the project include:

Cross-platform: This project can be compiled on different platforms, such as windows linux android stm32 embedded platforms.

Customizable CNN architecture: The framework provides flexibility in defining the layers, such as convolutional layers,active layer, pooling layers, fully connected layers,and softmax layer and so on, allowing users to tailor the network architecture to their specific needs.

Training and optimization: The project includes functions for training the CNN using backpropagation and gradient descent. Users can choose different optimization techniques, such as stochastic gradient descent (SGD) or Adam optimizer, to improve the training process. Evaluation and prediction: Once trained, the CNN model can be evaluated on test datasets, providing accuracy metrics and performance evaluation. Additionally, it supports making predictions on unseen data, enabling applications like image classification.

I have documented the code extensively, providing explanations and examples to help users understand the implementation and utilize the framework effectively. The project also includes sample cifar-10 datasets and an example script to guide users through the training process. I welcome contributions, feedback, and collaborations from the open-source community. Feel free to explore the project on GitHub [https://github.com/zhuchao-octopus/C-CNN], where you can find the complete source code, instructions, and further details.

Thank you for your time and interest in this project. I hope it proves to be a valuable resource for anyone interested in implementing CNNs in C.