https://github.com/ghazariann/cnn-lstm

https://github.com/ghazariann/cnn-lstm

Last synced: 8 days ago
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• Host: GitHub
• URL: https://github.com/ghazariann/cnn-lstm
• Owner: ghazariann
• Created: 2024-01-25T15:47:00.000Z (10 months ago)
• Default Branch: master
• Last Pushed: 2024-01-25T15:58:19.000Z (10 months ago)
• Last Synced: 2024-02-15T04:12:45.256Z (9 months ago)
• Language: Python
• Size: 8.86 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Video Classification System using CNN-LSTM

This project implements a video classification system leveraging a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, focusing on the classification of video data into predefined categories.

## Getting Started

### Requirements

```bash

pip install -r requirements

```

### Configuration

Edit the `params.json` file to specify the model and data processing parameters. The parameters include paths to data, model configurations, and training settings.

![Alt text](/resources/image.png)

## Data Organization

The project expects video data to be organized in a specific structure:

- The data directory should contain subdirectories, each representing a video class.

- Each subdirectory should contain `.avi` files corresponding to that class.

- The name of each subdirectory is used as the label for the videos inside it.

### Example Structure:

![Alt text](/resources/image-1.png)

## Usage

To run the video classification system:

1. Set up the `params.json` with the desired configuration.

2. Organize your video data as described above.

3. Execute the main script to start the training and classification process.

```bash

python main.py

````

## Demo

You can use the `demo.py` script to see the system in action. Simply modify the labels in the `activity_labels` with your own data and provide an instance like `demo.mpt` (which is me doing jumping jacks, and as you can see, the model correctly predicts it).

For more information about the model training process and results, please refer to the `report.pdf`.