pytorch-CortexNet  by Atcold

PyTorch implementation of the CortexNet predictive model

created 8 years ago
367 stars

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Project Summary

CortexNet provides a PyTorch implementation of predictive models for video analysis, specifically targeting researchers and engineers in computer vision. It offers implementations of PredNet, Model01, and Model02 (CortexNet), enabling the development and training of sophisticated video prediction systems.

How It Works

The project implements additive and modulatory feedback models, including PredNet and CortexNet, within a PyTorch framework. It supports training configurations for MatchNet and TempoNet, allowing for flexible experimentation with different network architectures and training strategies for video prediction tasks.

Quick Start & Requirements

  • Install scikit-video and tqdm via pip.
  • Use conda for environment management and installing dependencies from conda-forge.
  • Data preparation scripts (resize_and_split.sh, resize_and_sample.sh) are provided.
  • Training is initiated via main.py with mode selection (MatchNet, TempoNet).
  • GPU selection is managed via CUDA_VISIBLE_DEVICES=n.
  • Official documentation is available via the project website.

Highlighted Details

  • Implements PredNet, Model01 (additive feedback), and Model02 (modulatory feedback/CortexNet).
  • Supports MatchNet and TempoNet training configurations.
  • Includes utilities for plotting, experiment diffing, and visualization.
  • Provides scripts for data resizing, splitting, and sampling.

Maintenance & Community

No specific information on contributors, sponsorships, or community channels (like Discord/Slack) is present in the README.

Licensing & Compatibility

The README does not explicitly state the license type. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The README does not detail specific limitations, known bugs, or deprecation status. The project appears to be focused on research and may require significant effort for integration into production systems.

Health Check
Last commit

6 years ago

Responsiveness

1 week

Pull Requests (30d)
0
Issues (30d)
0
Star History
1 stars in the last 90 days

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