| # Import necessary libraries | |
| from transformers import AutoProcessor, AutoModelForZeroShotImageClassification | |
| from PIL import Image | |
| import requests | |
| import torch | |
| import matplotlib.pyplot as plt | |
| # Load the pre-trained model and processor | |
| checkpoint = "openai/clip-vit-large-patch14" | |
| model = AutoModelForZeroShotImageClassification.from_pretrained(checkpoint) | |
| processor = AutoProcessor.from_pretrained(checkpoint) | |
| # Load and display the image | |
| url = "https://cdn.akamai.steamstatic.com/steam/apps/1026420/header.jpg?t=1657716289" | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| plt.imshow(image) | |
| plt.show() | |
| # Specify candidate labels for zero-shot classification | |
| candidate_labels = ["tree", "car", "bike", "cat"] | |
| # Prepare inputs for the model | |
| inputs = processor(text=candidate_labels, images=image, return_tensors="pt", padding=True) | |
| # Make predictions | |
| outputs = model(**inputs) | |
| logits = outputs.logits_per_image # shape: [batch_size, num_classes] | |
| probs = logits.softmax(dim=1) # Convert to probabilities | |
| # Get and print the most likely class | |
| predicted_class_idx = probs.argmax(-1).item() | |
| predicted_class = candidate_labels[predicted_class_idx] | |
| print(f'Predicted class: {predicted_class}') | |