Create Zero-Shot Image Classification

Zero-Shot Image Classification

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+# 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 = "URL_of_the_image"
+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}')