So how does this work? Well, first we take an image and convert it into something that our model can use this involves breaking down the pixels into smaller pieces called “tokens” (like words in a sentence). Then, we feed those tokens into our AltCLIPVisionModel along with some other information like attention masks and position IDs.
The model then processes these inputs using its fancy neural network magic to generate an output that tells us what’s going on in the image this could be things like “a cat is sitting on a couch” or “there are trees in the background”. Pretty cool, right?
Here’s some code to help illustrate how we might use this method:
# Import necessary libraries
from PIL import Image # Importing the Image module from the PIL library
import requests # Importing the requests library to make HTTP requests
from transformers import AutoProcessor, AltCLIPVisionModel # Importing the AutoProcessor and AltCLIPVisionModel from the transformers library
# Load our model and processor from Hugging Face (a popular library for working with pre-trained models)
model = AltCLIPVisionModel.from_pretrained("BAAI/AltCLIP") # Loading the pre-trained AltCLIPVisionModel from the BAAI/AltCLIP repository
processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") # Loading the pre-trained AutoProcessor from the BAAI/AltCLIP repository
# Load an image using the PIL library and convert it to a format that our model can use (in this case, we're loading from a URL)
url = "http://images.cocodataset.org/val2017/000000039769.jpg" # Storing the URL of the image we want to use
image = Image.open(requests.get(url, stream=True).raw) # Using the requests library to make a GET request to the URL and opening the image using the Image module from PIL
# Preprocess the image using our processor (this involves converting it into a format that our model can use)
inputs = processor(images=image, return_tensors="pt") # Preprocessing the image using the AutoProcessor and storing the result in the inputs variable
# Run the forward pass through our model and get an output that tells us what's going on in the image!
outputs = model(**inputs) # Running the forward pass through our model using the inputs and storing the result in the outputs variable
last_hidden_state = outputs.last_hidden_state # Storing the final hidden state (i.e., the "output") from our model in the last_hidden_state variable
pooled_output = outputs.pooler_output # Storing another output from our model that provides information about the image as a whole in the pooled_output variable
It’s pretty cool how our computer can understand what’s going on in an image just by looking at it through the lens of language, right?