GitHub / FandosA / Normal_Flow_Prediction_Pytorch
Deep learning model to predict the normal flow between two consecutive frames, being the normal flow the projection of the optical flow on the gradient directions.
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FandosA%2FNormal_Flow_Prediction_Pytorch
Stars: 0
Forks: 0
Open issues: 0
License: None
Language: Python
Size: 236 KB
Dependencies parsed at: Pending
Created at: over 1 year ago
Updated at: 3 months ago
Pushed at: 3 months ago
Last synced at: 3 months ago
Commit Stats
Commits: 119
Authors: 1
Mean commits per author: 119.0
Development Distribution Score: 0.0
More commit stats: https://commits.ecosyste.ms/hosts/GitHub/repositories/FandosA/Normal_Flow_Prediction_Pytorch
Topics: autoencoder, cnn, cnn-model, convolutional-neural-networks, deep-learning, deep-neural-networks, encoder-decoder, encoder-decoder-architecture, encoder-decoder-model, normal-flow, optical-flow, pytorch, pytorch-cnn, pytorch-implementation, residual-blocks, residual-networks, residual-neural-network