If you’re interested in learning how technology enables machines to interpret both text and images simultaneously, you’re in the right place. Today, we’ll explore the workings of an intriguing tool from OpenAI known as CLIP, which stands for Contrastive Language-Image Pre-training.

Building Connections: text and images

CLIP is designed to combine text and images, merging them into a unified space. Picture a large playground where words and images interact seamlessly; that’s the environment CLIP constructs. This integration simplifies the process of associating text with images, and the other way around.

An expert in image comprehension

As CLIP develops this interactive space, it excels in deciphering images. It becomes adept at identifying the contents of a picture without requiring specialized training for that specific function. This capability is versatile, leading to fascinating applications such as answering questions about visual content, and locating images using textual queries.

Spotting image themes

CLIP demonstrates its intelligence without needing extensive knowledge. It can analyze an image and understand its theme immediately, a process known as zero-shot learning. CLIP achieved this capability by being trained on a vast array of text and images simultaneously, which streamlined its learning process.

The building wlocks: how CLIP works

CLIP consists of two primary components: one dedicated to interpreting text and the other to analyzing images. For images, it breaks them down into smaller segments for detailed examination. In contrast, its approach to text is more straightforward compared to some of the larger, more complex text analysis models.

The real magic occurs when CLIP translates the insights from both text and images into a common space where they can interact. This is akin to converting them into a shared language, allowing for an exchange between the two mediums.

CLIP model architecture

A quick test with CLIP

Let’s dive in and experiment with CLIP. We’ll utilize a straightforward Python library named CLIP for this purpose. The initial step is to ensure you have the library installed. So, let’s begin by setting up the CLIP library on your system.

pip install clip

Once you’ve got CLIP installed, here’s a simple example of how you can use it to find the similarity between text and images:

import clip
import torch
from PIL import Image

# Load the model
device = "cuda" if torch.cuda.is_available() else "cpu"
model, transform = clip.load('ViT-B/32', device)

# Prepare the inputs
image = transform(Image.open("image.jpg")).unsqueeze(0).to(device)
text = clip.tokenize(["a photo of a cat"]).to(device)

# Calculate features
with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)

# Pick the top 5 most similar labels for the image
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
similarity = (100.0 * image_features @ text_features.T).squeeze(0)
similarity = similarity.cpu().numpy()

print(f"Similarity score: {similarity}")

In this example, we start by loading the CLIP model and preparing our image and text inputs. Next, we prompt CLIP to generate features or embeddings for both the image and the text. After that, we compute a similarity score to assess how closely the text description aligns with the image.

This basic demonstration is just a taste of CLIP’s capabilities. The journey ahead is filled with limitless potential, so I encourage you to experiment with various images and text descriptions. You might uncover some fascinating correlations!

Playing with different models and patches

Now that we’ve seen what CLIP can do, let’s dive a tad deeper and see how it does it. CLIP can use different types of models to look at images and text. And for images, it has a quirky way of examining them – it chops them into patches, kind of like cutting a pizza into slices, and then looks at each slice closely.

Now, when it comes to models for images, CLIP had a play around with a couple of them: ResNet and Vision Transformer (ViT). Among these, it found its best buddy in a large version of Vision Transformer, shorty called ViT-L. It divides each image into 14 patches, each a tiny square of 336×336 pixels, and scrutinizes each patch to understand the image.

For text analysis, CLIP utilizes a model similar to GPT-2, but simpler. Imagine it as the younger, leaner sibling – not as complex, yet still effective. This text model is surprisingly efficient with only 63 million parameters and 8 attention heads, ensuring it comprehends text accurately.

Here’s where the process becomes even more interesting. Both models, for text and images, generate what are known as embeddings. Think of embeddings as unique codes representing the model’s interpretation of the text or image. CLIP ingeniously aligns these codes in a unified space using two projection matrices. These matrices act like translators, allowing the text and image embeddings to ‘converse’ in a common language.

The diversity in CLIP’s models and its method for processing image patches can be likened to different ice cream flavors. Each model offers a distinct approach. Whether you prefer straightforward models (akin to vanilla ice cream) or more intricate ones with varying patch sizes (like a chocolate and mint combination), CLIP provides options tailored to your specific needs.

Wrapping up

CLIP is like opening a door where words and pictures come together, leading to a playground of endless possibilities. It’s a glimpse into a future where finding a picture using a text description or getting answers to image-based questions becomes a breeze.

Excited about diving deeper? Check this tutorial for producing CLIP vectors with Vector Forge library. Also, stay tuned as we’ll be exploring more of such fascinating tools and how they can be a bridge between the world of words and pictures. Until then, happy exploring!

Categorized in:

Computer Vision, Deep Learning,

Last Update: 26/12/2023