Instructions to use buithanhdam02/LSTM_Resnet50_Attention_HAR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use buithanhdam02/LSTM_Resnet50_Attention_HAR with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://buithanhdam02/LSTM_Resnet50_Attention_HAR") - Notebooks
- Google Colab
- Kaggle
A model use to train with UCF101 dataset (30 first actions) in human action regconition tasks. you can visit my notebook here: https://www.kaggle.com/code/damthanh/resnet50-lstm-attention-ucf101/notebook
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