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Image features

Building an Image Feature Database in torchvision

!pip install superduperdb==0.0.12
!pip install torchvision

In this example, we show how to utilize a pre-trained network from torchvision to produce image features. The images are automatically fetched and stored in MongoDB. We use a subset of the CoCo dataset ( to illustrate the process.

Real-life use cases for creating a database of image features using a pre-trained network in torchvision:

  1. Image Search and Retrieval:

    • Use Case: Enhance image search capabilities in e-commerce platforms.
    • How: Generate image features for products using a pre-trained network. Store these features in a database for efficient image retrieval, making it easier for users to find similar products.
  2. Content-Based Recommendation Systems:

    • Use Case: Improve content recommendations in media streaming services.
    • How: Extract image features from movie or show frames. Store these features in a database to recommend content with similar visual characteristics to users based on their preferences.
  3. Facial Recognition in Security Systems:

    • Use Case: Strengthen facial recognition systems in security applications.
    • How: Utilize a pre-trained neural network to extract facial features from images. Store these features in a database for quick and accurate identification in security and surveillance scenarios.
  4. Medical Image Analysis:

    • Use Case: Assist in medical diagnostics through image analysis.
    • How: Extract features from medical images (X-rays, MRIs, etc.) using a pre-trained network. Store these features to aid in the development of diagnostic tools or systems for healthcare professionals.
  5. Automated Image Tagging:

    • Use Case: Streamline image organization in photo libraries or social media platforms.
    • How: Extract features from uploaded images using a pre-trained model. Use these features to automatically generate relevant tags, making it easier for users to search and categorize their photos.

These use cases demonstrate how creating a database of image features using torchvision can be applied across various domains to enhance functionality and improve user experiences. Guess what, all can be done with superduperdb like this example.

# Download the zip file
!curl -O

# Unzip the contents of the zip file (assuming the file is already downloaded)
!unzip -qq

Connect to Datastore

First, we need to establish a connection to a MongoDB datastore via SuperDuperDB. You can configure the MongoDB_URI based on your specific setup.

Here are some examples of MongoDB URIs:

  • For testing (default connection): mongomock://test
  • Local MongoDB instance: mongodb://localhost:27017
  • MongoDB with authentication: mongodb://superduper:superduper@mongodb:27017/documents
  • MongoDB Atlas: mongodb+srv://<username>:<password>@<atlas_cluster>/<database>
import os
from superduperdb import superduper
from superduperdb.backends.mongodb import Collection

# Get the MongoDB URI from the environment variable or use a default value
mongodb_uri = os.getenv("MONGODB_URI", "mongomock://test")

# SuperDuperDB, now handles your MongoDB database
# It just super dupers your database
db = superduper(mongodb_uri)

# Specify a collection named 'coco'
collection = Collection('coco')

Next, we include all image URIs in MongoDB. These URIs may include a mix of local file paths (file://...), web URLs (http...), and S3 URIs (s3://...). Once the URIs are added, SuperDuperDB automatically loads their content into MongoDB without the need for extra overhead or job definitions.

import glob
import random

from superduperdb import Document as D
from superduperdb.ext.pillow import pil_image as i

# Get a list of file URIs for all JPEG images in the 'valsmall2014' directory
uris = [f'file://{x}' for x in glob.glob('valsmall2014/*.jpg')]

# Insert documents into the 'coco' collection in the MongoDB database
db.execute(collection.insert_many([D({'img': i(uri=uri)}) for uri in uris], encoders=(i,))) # Here the image is encoded with pillow

To confirm the correct storage of images in the Datalayer, we can perform a verification check.

# Import the display function from the IPython.display module
from IPython.display import display

# Define a lambda function for displaying images with resizing to avoid potential Jupyter crashes
display_image = lambda x: display(x.resize((round(x.size[0] * 0.5), round(x.size[1] * 0.5))))

# Retrieve the 'img' attribute from the result of collection.find_one() using db.execute()
# Note: This assumes that db is an instance of a database connection wrapped with superduperdb
x = db.execute(collection.find_one())['img'].x

# Display the image using the previously defined lambda function

Let's build the torch + torchvision model using the TorchModel wrapper from SuperDuperDB. This allows for the incorporation of custom pre- and post-processing steps along with the model's forward pass.

# Import necessary libraries and modules from torchvision and torch
from torchvision import transforms
import torch
import torch.nn as nn
import torchvision.models as models

import warnings

# Import custom modules
from superduperdb.ext.torch import TorchModel, tensor

# Define a series of image transformations using torchvision.transforms.Compose
t = transforms.Compose([
transforms.Resize((224, 224)), # Resize the input image to 224x224 pixels (must same as here)
transforms.CenterCrop((224, 224)), # Perform a center crop on the resized image
transforms.ToTensor(), # Convert the image to a PyTorch tensor
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # Normalize the tensor with specified mean and standard deviation

# Define a preprocess function that applies the defined transformations to an input image
def preprocess(x):
return t(x)
except Exception as e:
# If an exception occurs during preprocessing, issue a warning and return a tensor of zeros
return torch.zeros(3, 224, 224)

# Load the pre-trained ResNet-50 model from torchvision
resnet50 = models.resnet50(pretrained=True)

# Extract all layers of the ResNet-50 model except the last one
modules = list(resnet50.children())[:-1]
resnet50 = nn.Sequential(*modules)

# Create a TorchModel instance with the Res

Net-50 model, preprocessing function, and postprocessing lambda
model = TorchModel(
postprocess=lambda x: x[:, 0, 0], # Postprocess by extracting the top-left element of the output tensor
encoder=tensor(torch.float, shape=(2048,)) # Specify the encoder configuration

To ensure the correctness of the model, let's test it on a single data point by setting one=True.

# Assuming x is an input tensor, you're making a prediction using the configured model
# with the one=True parameter specifying that you expect a single prediction result.
model.predict(x, one=True)

Now that the model is prepared, we can apply it to the images stored in the Datalayer.

# Assuming X is the input data, in this case, images ('img')
prediction_results = model.predict(
X='img', # Specify the input data (images)
db=db, # Provide the database connection or object
select=collection.find(), # Specify the data to be used for prediction (fetch all data from the collection)
batch_size=10, # Set the batch size for making predictions
max_chunk_size=3000, # Set the maximum size of data chunks processed at once
in_memory=False, # Indicate that the data is not loaded entirely into memory, processed in chunks
listen=True # Enable listening mode, suggesting real-time or asynchronous prediction

To confirm that the features were stored in the Datalayer, you can examine them in the _outputs.img.resnet50 field.

# Execute find_one() to retrieve a single document from the collection.
result = db.execute(collection.find_one())

# The purpose of unpack() is to extract or process the data