# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using (https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details


### Model Sources [optional]

<!-- Provide the basic links for the model. -->
Find the code in https://github.com/RKorzeniowski/BigBiGAN-PyTorch

## How to use


    def load_model(self,model_path):
        self.model.load_state_dict(torch.load(model_path))

    def init_GAN(self):
        print('load the pre-trained model')

        data_set = self.config.ds_name
        if 'CIFAR10' in data_set:
            model_path = 'checkpoint_150.pth'
        elif 'MNIST' in data_set:
            model_path = 'checkpoint_150.pth'

        self.load_model(model_path)




{"language": ["zh"], "tags": ["code"], "datasets": ["mnist"], "pipeline_tag": "unconditional-image-generation"}
code unconditional-image-generation zh dataset:mnist region:us
TAGS
#code #unconditional-image-generation #zh #dataset-mnist #region-us 

# Model Card for Model ID



This modelcard aims to be a base template for new models. It has been generated using (URL

## Model Details


### Model Sources [optional]


Find the code in URL

## How to use


    def load_model(self,model_path):
        URL.load_state_dict(URL(model_path))

    def init_GAN(self):
        print('load the pre-trained model')

        data_set = URL.ds_name
        if 'CIFAR10' in data_set:
            model_path = 'checkpoint_150.pth'
        elif 'MNIST' in data_set:
            model_path = 'checkpoint_150.pth'

        self.load_model(model_path)




# Model Card for Model ID



This modelcard aims to be a base template for new models. It has been generated using (URL ## Model Details ### Model Sources [optional]


Find the code in URL ## How to use


    def load_model(self,model_path):
        URL.load_state_dict(URL(model_path))

    def init_GAN(self):
        print('load the pre-trained model')

        data_set = URL.ds_name
        if 'CIFAR10' in data_set:
            model_path = 'checkpoint_150.pth'
        elif 'MNIST' in data_set:
            model_path = 'checkpoint_150.pth'

        self.load_model(model_path)
TAGS
#code #unconditional-image-generation #zh #dataset-mnist #region-us 
 # Model Card for Model ID



This modelcard aims to be a base template for new models. It has been generated using (URL ## Model Details ### Model Sources [optional]


Find the code in URL ## How to use


    def load_model(self,model_path):
        URL.load_state_dict(URL(model_path))

    def init_GAN(self):
        print('load the pre-trained model')

        data_set = URL.ds_name
        if 'CIFAR10' in data_set:
            model_path = 'checkpoint_150.pth'
        elif 'MNIST' in data_set:
            model_path = 'checkpoint_150.pth'

        self.load_model(model_path)
TAGS
#code #unconditional-image-generation #zh #dataset-mnist #region-us 
# Model Card for Model ID



This modelcard aims to be a base template for new models. It has been generated using (URL## Model Details### Model Sources [optional]


Find the code in URL## How to use


    def load_model(self,model_path):
        URL.load_state_dict(URL(model_path))

    def init_GAN(self):
        print('load the pre-trained model')

        data_set = URL.ds_name
        if 'CIFAR10' in data_set:
            model_path = 'checkpoint_150.pth'
        elif 'MNIST' in data_set:
            model_path = 'checkpoint_150.pth'

        self.load_model(model_path)