Video Prediction and Mask Segmentation

Overview

This project focuses on the challenging tasks of video prediction and mask segmentation. It employs ConvLSTM (Convolutional Long Short-Term Memory) networks for predicting future frames in a video sequence and segments objects by generating masks. This approach can be particularly useful in applications such as video surveillance, autonomous driving, and dynamic scene understanding.

Features

• Video Prediction: Uses ConvLSTM to predict future frames based on past sequences.
• Mask Segmentation: Segments objects in video frames to understand scene dynamics better.
• Customizable Configurations: Offers configuration options for prediction and segmentation tasks.
• Dataset Sorting in Prediction: Implements sorting of video folders in PredictionDataset for streamlined data processing.

Project Structure

• configs/: Configuration files for prediction and segmentation models.
• predictor/: Implementation of the ConvLSTM predictor model.
• segmenter/: Implementation of the segmentation model.
• utils/: Utility scripts for dataset handling and other common functions.
• predict_hidden.py: Script for running predictions with hidden configurations.
• requirements.txt: Lists all the dependencies required to run the project.
• train_predictor.py: Script for training the ConvLSTM predictor model.
• train_segmenter.py: Script for training the segmentation model.

Getting Started

Prerequisites

Ensure you have Python 3.x installed on your system. You can install all the dependencies using:

pip install -r requirements.txt