CLIP (Contrastive Language–Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning. It learns from unfiltered, highly varied, and highly noisy data, and is intended to be used in a zero-shot manner. CLIP struggles on more abstract or systematic tasks such as counting the number of objects in an image and on more complex tasks such as predicting how close the nearest car is in a photo.The model allows people to design their own classifiers and removes the need for task-specific training data.
# Embed data# We'll use an example dataset of images of animals and cities:from datasets import load_datasetdata = load_dataset( "jamescalam/image-text-demo", split="train")from transformers import CLIPProcessor, CLIPModelimport torchmodel_id = "openai/clip-vit-base-patch32"processor = CLIPProcessor.from_pretrained(model_id)model = CLIPModel.from_pretrained(model_id)# move model to device if possibledevice = 'cuda' if torch.cuda.is_available() else 'cpu'model.to(device)# ClIP allows for both text and image embeddingsdef create_text_embeddings(text): text_embedding = processor(text=text, padding=True, images=None, return_tensors='pt').to(device) text_emb = model.get_text_features(**text_embedding) return text_emb[0]def create_image_embeddings(image): vals = processor( text=None, images=image, return_tensors='pt')['pixel_values'].to(device) image_embedding = model.get_image_features(vals) return image_embedding[0]# We will embed the images and search with textfrom IPython.display import Image def apply_vectorization(data): data["image_embeddings"] = create_image_embeddings(data["image"]) return datadata = data.map(apply_vectorization)# add an id column for easy indexing laterids = [str(i) for i in range(0, data.num_rows)]data = data.add_column("id", ids)vectors = []for i in range(0, data.num_rows): d = data[i] vectors.append({ "id": d["id"], "values": d["image_embeddings"], "metadata": {"caption": d["text"]} })index.upsert( vectors=vectors, namespace="ns1")
query = "Show me a photo of a city"x = create_text_embeddings(query).tolist()results = index.query( namespace="ns1", vector=x, top_k=3, include_values=False, include_metadata=True)print(results)def id_to_image_helper(id, data): # given id, renders the images and captions # resizes in order to speed up showing the image image = data[int(id)] print(image["text"]) return image["image"].resize((500, 500))# view a specific result using the helper id_to_image_helper(results["matches"][0]["id"], data)
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