How to Create a Image Classifier with OpenAI CLIP and Python

Posted on by Panther

How to Create an Image Classifier with OpenAI CLIP and Python

In recent years, deep learning models have become increasingly powerful in tasks such as image recognition and natural language processing. OpenAI’s CLIP (Contrastive Language-Image Pretraining) is one such model that can perform various visual tasks. It combines a convolutional neural network (CNN) trained on a large dataset of images with a transformer-based language model trained on a large dataset of text.

In this tutorial, we will learn how to create an image classifier using OpenAI CLIP and Python. We will walk through the process of installing the necessary libraries, loading the CLIP model, preprocessing images, and finally using the model to classify images.

Prerequisites

To follow along with this tutorial, you will need:

  • Python installed on your machine (version 3.6 or higher)
  • Familiarity with Python programming language, including basic knowledge of libraries such as numpy and PIL
  • Basic understanding of deep learning concepts

Step 1: Install Dependencies

To get started, open your terminal and create a new Python environment (optional but recommended). Then, install the required dependencies by running the following command:

pip install torch torchvision pillow

Step 2: Import Libraries

In this step, we will import the necessary libraries for our project. We will be using torch for the deep learning framework, PIL for image preprocessing, and numpy for various array operations. Additionally, we will import the clip module from the torchvision library, which provides the pre-trained CLIP model.

import torch
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
from torchvision.models import clip

Step 3: Load the CLIP Model

Next, we need to load the pre-trained CLIP model. OpenAI has provided several variations of the model, each trained on different amounts of data. For this tutorial, we will use the ViT-B/32 variant, which performs well on various tasks.

device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)

We check if a GPU is available and set the device accordingly. Then, we load the ViT-B/32 variant of the model along with the corresponding preprocess function. The preprocess function performs image preprocessing and normalization required by the CLIP model.

Step 4: Preprocess Images

Before we can classify images, we need to preprocess them using the preprocess function we loaded in the previous step. The function takes a PIL image and returns a tensor suitable for input to the CLIP model.

def preprocess_image(image_path):
    image = Image.open(image_path).convert("RGB")
    image = preprocess(image).unsqueeze(0).to(device)
    return image

The preprocess_image function takes the path to an image file as input. We open the image file using PIL, convert it to RGB color space, preprocess it using the preprocess function, and finally convert it to a tensor. The tensor is then unsqueezed to add an extra dimension and moved to the device (CPU or GPU) we specified earlier.

Step 5: Classify Images

Now that we have a function to preprocess images, we can define a function to classify them using the CLIP model.

def classify_image(image_path):
    image = preprocess_image(image_path)
    with torch.no_grad():
        image_features = model.encode_image(image)
        text_features = model.encode_text(clip.tokenize(["a photo of a dog"]))
        similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
    return similarity

In the classify_image function, we first preprocess the image using the preprocess_image function defined earlier. We then encode the image and a descriptive text as features using the encode_image and encode_text functions of the CLIP model, respectively. Finally, we compute the similarity between the image and the text by performing matrix multiplication followed by softmax normalization.

It’s worth mentioning that the encode_text function expects a tensor of token IDs as input. We use the clip.tokenize function to convert the text into a tensor of token IDs.

Step 6: Test the Classifier

To test our image classifier, let’s classify a sample image. Create a new Python file, and add the following code:

image_path = "path/to/your/image.jpg"
similarity = classify_image(image_path)
print(similarity)

Replace "path/to/your/image.jpg" with the path to an image you want to classify. Run the Python file, and you should see a similarity score printed on the console.

The similarity score represents the model’s confidence in the image being similar to the provided text. Higher scores indicate higher similarity.

Conclusion

In this tutorial, we learned how to create an image classifier using OpenAI’s CLIP model and Python. We installed the necessary dependencies, loaded the pre-trained CLIP model, preprocessed images, and used the model to classify images.

CLIP is a powerful model that can be used for various visual tasks, such as image recognition, image generation, and more. With its ability to understand both images and text, it opens up new possibilities for creative applications.

Feel free to explore the CLIP model further and experiment with different variations to achieve better results. Happy classifying!

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