Combine ResNet-50 embeddings with metadata for improved accuracy in your image search system

Step 1: Install Required Libraries

Ensure you have the necessary libraries:

• Install Milvus and its Python SDK pymilvus.
• Install libraries for handling images and metadata (e.g., TensorFlow, Scikit-learn).

pip install pymilvus tensorflow scikit-learn

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Step 2: Set Up Milvus

1. Start Milvus:

   • Install and run Milvus locally or use a hosted service like Zilliz Cloud.

   docker-compose up -d
   

2. Connect to Milvus:

   • Use the Python SDK to connect to the Milvus server.

   from pymilvus import connections
   
   # Connect to Milvus
   connections.connect("default", host="127.0.0.1", port="19530")
   

3. Create a Collection:

   • Design a schema to store image embeddings and metadata.

   from pymilvus import CollectionSchema, FieldSchema, DataType, Collection
   
   # Define schema
   fields = [
       FieldSchema(name="image_embedding", dtype=DataType.FLOAT_VECTOR, dim=2048),  # ResNet-50 embeddings
       FieldSchema(name="category", dtype=DataType.VARCHAR, max_length=50),        # Categorical metadata
       FieldSchema(name="brand", dtype=DataType.VARCHAR, max_length=50),           # Categorical metadata
       FieldSchema(name="price", dtype=DataType.FLOAT),                            # Numerical metadata
   ]
   schema = CollectionSchema(fields, description="Image search collection")
   
   # Create collection
   collection = Collection("ecommerce_image_search", schema)
   

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Step 3: Index Data

1. Extract and Encode Features:

   • Use ResNet-50 to extract embeddings and encode metadata (as explained in earlier steps).

2. Insert Data into Milvus:

   • Combine embeddings with metadata and add them to Milvus.

   # Example data
   image_embedding = [0.1, 0.2, ..., 0.9]  # Example 2048-d embedding
   category = "shoes"
   brand = "Nike"
   price = 99.99
   
   # Insert data into Milvus
   data = [[image_embedding], [category], [brand], [price]]
   collection.insert(data)
   print("Data inserted successfully")
   

3. Create Index for Faster Search:

   • Create a vector index for the image_embedding field to optimize similarity search.

   index_params = {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 128}}
   collection.create_index(field_name="image_embedding", index_params=index_params)
   print("Index created successfully")
   

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Step 4: User Input (Query)

1. Image Upload:

   • Extract ResNet-50 embedding from the uploaded image.

   query_image_embedding = extract_features("uploaded_image.jpg")
   

2. Metadata Filters:

   • Get metadata selections (e.g., category: "shoes", brand: "Nike").
   • Convert filters to SQL-like queries for Milvus.

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Step 5: Perform Hybrid Search

1. Combine Image and Metadata Search:

   • Milvus supports hybrid searches using metadata filters.

   # Define a query
   search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
   filters = "category == 'shoes' && brand == 'Nike'"  # User-selected metadata filters
   
   # Perform search
   results = collection.search(
       data=[query_image_embedding],  # Input vector
       anns_field="image_embedding",  # Vector field name
       param=search_params,
       limit=10,  # Number of results
       expr=filters  # Metadata filter
   )
   
   # Display results
   for hit in results[0]:
       print(f"ID: {hit.id}, Score: {hit.score}, Metadata: {hit.entity}")
   

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Step 6: Return Results

1. Retrieve Matching Products:

   • Use the IDs of the search results to fetch additional product details (e.g., names, images) from your database.

2. Display Results:

   • Show visually similar products filtered by the selected metadata on the front end.

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Step 7: Refine and Optimize

1. Weighting Embeddings and Metadata:

   • If image features are more critical, assign higher weight to embeddings.

   combined_embedding = 0.8 * image_embedding + 0.2 * metadata_vector
   

2. Tune Milvus Parameters:

   • Experiment with nlist and nprobe in the index parameters for better speed and accuracy.

3. Monitor Performance:

   • Regularly update indexes and handle metadata updates efficiently.

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Advantages of Using Milvus

• Efficient handling of large-scale image and metadata data.
• Native support for hybrid searches (combining vectors and metadata).
• Scalable and integrates well with machine learning workflows.

Would you like more details on any specific step or a complete code example?

Thank you