Metadata-Version: 2.1
Name: vpredicto
Version: 0.1.10
Summary: A library for video frame prediction using PredRNN++, MIM, and Causal LSTM.
Home-page: https://github.com/karimsaqer/predicto
Author: Team 18
Author-email: Karim.Saqer01@eng-st.cu.edu.eg
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: torch >=1.7.0
Requires-Dist: torchvision >=0.8.1
Requires-Dist: pytorch-lightning >=1.0.0
Requires-Dist: scikit-image >=0.17.2
Requires-Dist: numpy >=1.19.2

# vPredicto

**Predicto** is a Python library for video frame prediction, featuring three state-of-the-art models: PredRNN++, MIM, and Causal LSTM. This library is designed to cater to both expert and non-expert users, providing an API for developers and a simple interface for non-experts.

## Features

- Three video frame prediction models: PredRNN++, MIM, and Causal LSTM.
- Easy-to-use interface for training and testing models.
- Supports custom dataloaders or default to MovingMNIST dataset.
- Pre and post-processing for input and output in each model.

## Installation

```sh
pip install vpredicto
```

## Usage

## Quick Start
```sh
from predicto import PredRNN, MIM, CausalLSTM, Predicto

# Create a model object
model_object = MIM()

# Initialize Predicto with the model object
model = Predicto(model_object)

# Train the model
model.train(train_loader)

# Test the model
model.test(test_loader)
```

## Models
- PredRNN++: A recurrent neural network model for video frame prediction.
- MIM: Memory In Memory network for spatiotemporal predictive learning.
- Causal LSTM: A causal LSTM model for video frame prediction.
