Image Labeling with Python and OpenAI CLIP: A Step-by-Step Guide

In this tutorial, we will walk you through the process of implementing an image labeling solution using Python and OpenAI's CLIP model. CLIP (Contrastive Language-Image Pretraining) is a state-of-the-art AI model from OpenAI that combines the power of natural language processing and computer vision, making it effectively useful for various tasks, such as image labeling.

Table of Contents

  1. Prerequisites
  2. Installation
  3. Data Preparation
  4. Building the Image Labeler
  5. Running the Image Labeler
  6. Conclusion

1. Prerequisites

Before diving into the implementation, make sure you have the following installed on your machine:

  • Python 3.7 or later
  • PyTorch 1.7.1 or later
  • torchvision 0.8.2 or later

2. Installation

First, we need to install the necessary Python packages. Run the following command in your terminal:

pip install torch torchvision openai ftfy regex

This will install PyTorch, torchvision, OpenAI, and other required libraries.

3. Data Preparation

For this tutorial, we'll use a dataset of images that you want to label. The dataset can be your own collection or from any publicly available sources.

Create a folder named images and place all the images you want to label inside it.

4. Building the Image Labeler

To build the image labeler in Python, follow these steps:

  1. Import the required libraries:
import torch
import torchvision.transforms as T
from PIL import Image
import openai
import ftfy
import regex
  1. Load the CLIP model and tokenizer:
model, preprocess = torch.hub.load('openai/clip', 'ViT-B/32', jit=False)
tokenizer = openai.CLIPTokenizer()
  1. Define the preprocessing function:
def preprocess_image(image_path, preprocess):
    image ='RGB')
    return preprocess(image)
  1. Create a function to generate image labels:
def generate_image_labels(image_tensor, model, tokenizer, max_labels=5):
    with torch.no_grad():
        image_features = model.encode_image(image_tensor)
        label_logits = model.logits_per_image(image_features)
        label_probs = label_logits.softmax(dim=-1)

    label_ids = label_probs.argsort(descending=True)
    label_probs_sorted = label_probs[label_ids]
    top_labels = [tokenizer.decode(label_id.item()) for label_id in label_ids[:max_labels]]

    return top_labels

5. Running the Image Labeler

Now that we have our image labeler in place, it's time to run it on our dataset:

  1. Iterate through the images in the images folder:
import os

image_folder = 'images'
for image_file in os.listdir(image_folder):
    image_path = os.path.join(image_folder, image_file)
    # Preprocess the image
    image_tensor = preprocess_image(image_path, preprocess).unsqueeze(0)
    # Generate image labels
    labels = generate_image_labels(image_tensor, model, tokenizer)
    # Print the image file name and its labels
    print(f"{image_file}: {', '.join(labels)}")

This will print out the image file names and their corresponding labels.

6. Conclusion

In this tutorial, we have demonstrated how to create an image labeling solution using Python and OpenAI's CLIP model. This powerful AI model can be adapted for various applications, such as image captioning, object recognition, and more.

As a next step, you can experiment with different CLIP models or fine-tune the model on your specific dataset to improve the image labeling performance.

An AI coworker, not just a copilot

View VelocityAI