Hello, and welcome to this video on Understanding Image Captioning with Meta's Llama.
After watching this video, you will be able to define image captioning, explain how multimodal
models work, and implement an image captioning model in Python using IBM Watson X.
Imagine you have an archive of 2,000 pictures of your vacations during the past 15 years.
The pictures are stored on your computer, and you want to classify them based on year and holiday destination.
Manually, it would take you many hours to complete this work, and the information might not be accurate.
If you choose to use image captioning, you can classify the same vacation pictures in
minutes and with high accuracy.
Image captioning is the process of automatically generating textual descriptions of images.
It uses computer vision and natural language processing, NLP techniques, to generate meaningful information for humans.
The process of creating image captions using a multimodal large-language model, LLM, comprises three key stages.
Input processing, image validation and encoding, multimodal LLM processing.
During input processing, the system receives the image and possibly some accompanying text, like a prompt or question.
During image validation and encoding, the image is interpreted by the model, which only
thinks in numbers or vectors.
The last stage is multimodal LLM processing, where the model fuses visual and textual information into a caption.
Let's try to understand this process more closely with the help of the following diagram.
Input processing is the first stage of image captioning.
The image captioning system accepts two types of input, an image that needs to be captioned
and a text prompt or query that guides the captioning process.
During this stage, the image is sent for pre-processing, where it's normalized, resized, and optimized for the model.
Meanwhile, the text prompt provides context or specifies what aspects of the image to focus on.
After processing, there's a critical validation step where the image captioning system checks
if the processed image is valid for further processing.
During validation, the system verifies that the processed image meets technical requirements,
contains detectable features, and is suitable for the model.
If the image passes validation, it proceeds to encoding, where it's converted into a base64 encoded string.
This encoding transforms visual data into a text-based format that the language model can process.
This embedding captures objects, scenes, relationships, styles, etc.
Multimodal LLM processing is the heart of the image captioning system.
This is where all the magic happens.
The visual encoder component extracts meaningful visual features from the encoded image.
Simultaneously, the text prompt is converted into numerical vectors through text embedding.
The multimodal fusion layer combines visual features and text embeddings into a unified representation.
Finally, the language generation component crafts natural language text based on this fused information.
The result is a generated caption that describes the image in a way that's responsive to the text prompt.
Implementing an image captioning model in Python typically involves combining a CNN
convolutional neural network to encode the image and an RNN recurrent neural network
or a transformer-based decoder to generate the caption.
Let's look at an image caption implementation using Meta's Llama 4 Maverick model, accessed through IBM's WatsonX platform.
This powerful large-language model has 90 billion parameters specifically designed for
visual reasoning tasks.
We first import the necessary libraries for API authentication, image processing, and
model interaction to implement the image caption model.
Then we set up the credentials to access IBM WatsonX.
The API key would normally be added for secure access.
An instance of API client will be created, enabling us to interact with the IBM WatsonX API.
Next, we prepare our test images by encoding them properly.
To encode these images so that the LLM can process them, we encode the images to bytes
that we then decode to UTF-8 representation.
After encoding the images, we initialize the Llama model using the appropriate parameters.
Now that our images can be passed to the LLM, let's set up an instance of the Llama 4 Maverick
17b-128e-instruct-fp8 model through IBM WatsonX AI library.
We define a function to send images with queries to the model.
This function builds a message structure that combines text and image data.
Notice how we create a message with the user role, include two content elements, text,
our query, and the encoded image.
Send this combined message to the model and extract and return the response text.
The model simultaneously processes the image using computer vision techniques and the text prompt.
It uses attention mechanisms to relate visual features to language concepts and then generates appropriate descriptive text.
Now, we can loop through our images to see the text descriptions produced by the model
in response to the query, describe the photo.
In this video, you learned that combining computer vision with natural language processing
creates powerful tools for understanding visual content.
The image captioning process with a multimodal large language model, LLM, comprises three
main stages, input processing, image validation and encoding, and multimodal LLM processing.
Input processing receives and prepares the image and optional text prompt.
Image validation and encoding validates and converts the image into a format, base 64, suitable for the model.
Multimodal LLM processing combines visual and textual information to generate a descriptive caption.
The core of the image captioning system uses components like visual encoders, text embedding,
fusion layers, and language generation tools to produce captions tailored to prompts.
Implementing an image captioning system using Meta’s Llama 4 Maverick model via IBM Watson
X involves importing libraries and authenticating access, encoding images, and preparing prompts,
sending combined image text messages to the model, and extracting descriptive text from the model's response.