ECON: Explicit Clothed humans Optimized via Normal integration

20.02.2024
ECON: Explicit Clothed humans Optimized via Normal integration

You can follow these steps to integrate the code https://github.com/YuliangXiu/ECON.git into Google Colab:

  1. Open Google Colab and create a new notebook.
  2. Click on “Runtime” in the top menu, then select “Change runtime type”.
  3. In the pop-up window, select “GPU” as the hardware accelerator.
  4. Click on “Connect” and wait for the virtual environment to load.
  5. Once the virtual environment is ready, click on “Terminal” in the top menu.
  6. In the terminal, run the following command to clone the repository: !git clone https://github.com/YuliangXiu/ECON.git
  7. Wait for the repository to finish cloning.
  8. Navigate to the cloned repository folder by running the command cd ECON.
  9. You can now use the code in the repository in your Google Colab notebook.

Note that you may need to install any required packages or dependencies before running the code. You can do this using pip or conda in the terminal.

i can provide you with a template that you can use as a starting point. here’s a sample python script that uses the icon library to train and test a neural network:

import icon

# load the dataset
x_train, y_train, x_test, y_test = icon.load_dataset()

# define the neural network architecture
model = icon.model(
    icon.sequential([
        icon.dense(units=32, activation='relu', input_shape=(x_train.shape[1],)),
        icon.dense(units=10, activation='softmax')
    ])
)

# train the model
model.compile(loss='categorical_crossentropy', optimizer='adam')
model.fit(x_train, y_train, epochs=10, batch_size=32)

# evaluate the model
loss, accuracy = model.evaluate(x_test, y_test)
print('loss:', loss)
print('accuracy:', accuracy)

in this script, we first load the dataset using the icon.load_dataset function. then, we define a neural network


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