A library for video frame prediction using PredRNN++, MIM, and Causal LSTM.
Project description
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
pip install vpredicto
Usage
Quick Start
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
vpredicto-0.1.5.tar.gz
(7.2 kB
view details)
Built Distribution
File details
Details for the file vpredicto-0.1.5.tar.gz
.
File metadata
- Download URL: vpredicto-0.1.5.tar.gz
- Upload date:
- Size: 7.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.11.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 468ff504cf8f31b4b5253fe2415511d47936d45c86400ffb521ec2ffb902abed |
|
MD5 | d2b0c9a06d91a02b9bad13f676c7330c |
|
BLAKE2b-256 | 5f12c4d63d91dde1c68868704fd2a15adbd7212f77f5451f16617ebc239512bc |
File details
Details for the file vpredicto-0.1.5-py3-none-any.whl
.
File metadata
- Download URL: vpredicto-0.1.5-py3-none-any.whl
- Upload date:
- Size: 9.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.11.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2498730e23089c73098e4c1da6b8d07d8cf0a5e3a08aeca5b575be24054974e5 |
|
MD5 | 743ea92e8abbc96ed33e861f6f744f7f |
|
BLAKE2b-256 | a8d5a7c67c6d7f37e2e32b8481d3a0196dcfe69a15694a58728d5d214ea993c1 |