Finding images with words

In this section, we will first train a CLIP model that we implemented in the previous sections. We will then use the trained model to retrieve images given a query. Finally, we will use a pre-trained CLIP model to perform image searches and zero-shot predictions.

Training a CLIP model

Let’s train a CLIP model in the following steps:

1. First, we create a CLIP model and move it to system device (either a GPU or CPU):

   >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
   >>> model = CLIPModel().to(device)
   

2. Next, we initialize an Adam optimizer to train the model and set the learning rate:

   >>> optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
   

3. As we did in previous chapters, we define the following training function to update the model:

   >>> def train(model, dataloader, optimizer):
           model.train()...