https://github.com/sayakpaul/action-recognition-in-tensorflow

Contains additional materials for two keras.io blog posts.
https://github.com/sayakpaul/action-recognition-in-tensorflow

action-recognition cnn-rnn keras sequence-models tensorflow transformers ucf-101 video-classification video-models

Last synced: 14 days ago
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Contains additional materials for two keras.io blog posts.

• Host: GitHub
• URL: https://github.com/sayakpaul/action-recognition-in-tensorflow
• Owner: sayakpaul
• License: apache-2.0
• Created: 2021-05-30T01:49:50.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-09-12T03:03:37.000Z (over 3 years ago)
• Last Synced: 2025-01-10T13:05:18.364Z (16 days ago)
• Topics: action-recognition, cnn-rnn, keras, sequence-models, tensorflow, transformers, ucf-101, video-classification, video-models
• Language: Jupyter Notebook
• Homepage:
• Size: 19.6 MB
• Stars: 16
• Watchers: 3
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Action-Recognition-in-TensorFlow

Contains additional materials for the following keras.io blog posts: 

* [Video Classification with a CNN-RNN Architecture](https://keras.io/examples/vision/video_classification/)

* [Video Classification with Transformers](https://keras.io/examples/vision/video_transformers/)

## Notebooks

* `Data_Preparation_UCF101.ipynb`: Performs the initial data preparation steps on the [UCF101 dataset](https://www.crcv.ucf.edu/data/UCF101.php).

* `Data_Prep_Transformers.ipynb` Performs additional data preparation steps for Transformers.

* `Video_Classification.ipynb`: Original notebook submitted for the [PR](https://github.com/keras-team/keras-io/pull/478). 

* `Video_Classification_w_Transformers`: Original notebook submitted for the [PR](https://github.com/keras-team/keras-io/pull/488). 

## Acknowledgements

* François Chollet for providing guidance during the development.

* [ML-GDE](https://developers.google.com/programs/experts/) program for providing GCP credits that helped me run the initial experiments.