https://github.com/savitar-hub/image-classification

Notebooks on how image classification can help businesses make better decisions and improve the customer experience.
https://github.com/savitar-hub/image-classification

image-classification image-processing jupyter-notebook numpy opencv pandas pytorch tensorflow

Last synced: 17 days ago
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Notebooks on how image classification can help businesses make better decisions and improve the customer experience.

• Host: GitHub
• URL: https://github.com/savitar-hub/image-classification
• Owner: Savitar-Hub
• Created: 2023-02-20T18:23:44.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-02-21T13:14:58.000Z (almost 2 years ago)
• Last Synced: 2024-11-06T10:12:31.598Z (2 months ago)
• Topics: image-classification, image-processing, jupyter-notebook, numpy, opencv, pandas, pytorch, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 985 KB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Image Classification

**Image classification** is a technology that uses algorithms to analyze visual data and can help businesses improve product recommendations, enhance marketing efforts, streamline operations, and enhance the customer experience.

Some use cases of image classification:

🛍️ Improve **product recommendations** by analyzing customer images as well as better understand their preferences.

📈 **Enhance marketing efforts** by analyzing the visual content of a company's website or social media accounts to create more engaging content and improve marketing effectiveness.

🤖 **Automating quality control** or **inventory management**, which can reduce manual labor and increase efficiency 🚀.

😃 Enhance the **customer experience** by analyzing customer photos of a product to gain insights into product usage and areas for improvement.

### Project Structure

There are various classification tasks, which could have a direct application into businesses:

- [Food Classification](./food-classification/) - EXAMPLE CASE: which food is posted in social media about our restaurant so we know which recipes are having an impact on customer satisfaction.

Also, inside of the [models](./models/) folder, you have implementation of some of the most popular algorithms for image classification, as well as information (papers) related to them. The models created are the following:

- **Efficientnet**

- **Googlenet**

- **Mobilenet**

- **Regnet**

- **Resnet**

- **Shufflenet**

- **Vgg**