README.md

RNN-CNN Video Extraction

This repository contains a project focused on extracting features from video data using a 4-layer Convolutional Neural Network (CNN). The primary objective is to develop and implement a CNN architecture capable of efficiently processing video frames to extract meaningful features for further applications, such as action recognition, object detection, and video summarization.

Features

• 4-Layer CNN Architecture: A well-structured 4-layer CNN designed for robust feature extraction from video data.
• Frame Preprocessing: Tools for preprocessing video frames, including resizing, normalization, and augmentation techniques.
• Training Pipeline: A comprehensive training pipeline with customizable parameters for learning rate, batch size, and epochs.
• Evaluation Metrics: Implementation of various evaluation metrics to assess the performance of the model.
• Sample Datasets: Scripts to download and preprocess sample video datasets for training and testing.
• Inference Module: A module to perform inference on new video data using the trained CNN model.

Getting Started

To get started with the project, follow the steps below:

1. Clone the Repository

   git clone https://github.com/Yassa122/VideoFeatureExtractionUsingDeepLearning.git
   cd RNN-CNN-Video-Extraction

2. Install Dependencies

   pip install -r requirements.txt

3. Prepare Dataset

   • Download a sample video dataset and place it in the data/ directory.
   • Use the provided scripts in the scripts/ directory to preprocess the dataset.

4. Train the Model

   python train.py --config config/train_config.yaml

5. Evaluate the Model

   python evaluate.py --config config/eval_config.yaml

6. Run Inference

   python inference.py --video_path path/to/video.mp4 --output_path path/to/output

Project Structure