| tags: | |
| - video-prediction | |
| - moving-mnist | |
| - video-to-video | |
| license: cc0-1.0 | |
| pipeline_tag: video-to-video | |
| ## Tensorflow Keras Implementation of Next-Frame Video Prediction with Convolutional LSTMs 📽️ | |
| This repo contains the models and the notebook [on How to build and train a convolutional LSTM model for next-frame video prediction](https://keras.io/examples/vision/conv_lstm/). | |
| Full credits to [Amogh Joshi](https://github.com/amogh7joshi) | |
| ## Background Information | |
| The [Convolutional LSTM](https://papers.nips.cc/paper/2015/file/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf) architectures bring together time series processing and computer vision by introducing a convolutional recurrent cell in a LSTM layer. This model uses the Convolutional LSTMs in an application to next-frame prediction, the process of predicting what video frames come next given a series of past frames. | |
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| ## Training Dataset | |
| This model was trained on the [Moving MNIST dataset](http://www.cs.toronto.edu/~nitish/unsupervised_video/). | |
| For next-frame prediction, our model will be using a previous frame, which we'll call `f_n`, to predict a new frame, called `f_(n + 1)`. To allow the model to create these predictions, we'll need to process the data such that we have "shifted" inputs and outputs, where the input data is frame `x_n`, being used to predict frame `y_(n + 1)`. | |
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