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dejanseo/
shopping-intent

Name: shopping-intent
Availability: InStock

tflite

image-classification

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Google’s Shopping Intent Classifier

Model Overview

This model is a multi-label image classification model extracted from Chrome. It's Google’s custom convolutional neural network in TensorFlow Lite v3 format based on mobilenet V3 small model. It can be deployed in an automated pipeline capable of classifying product images in bulk. The model is designed to determine whether an image is optimized for shopping intent and can classify images into one of four categories related to shopping intent.

Model Details

Model Name: shopping_intent_x_sensitivity_classifier
Checkpoint: mobilenet_v3_small_224_04132253_ckpt_3006395
Description: The model is MLIR Converted and classifies whether an image has shopping intent or is sensitive.
Model Author: lens-proactive-dev
Framework: TensorFlow Lite v3
Architecture: MobileNet V3 Small

Inputs

name: normalized_input_image_tensor
tensor: float32[1,224,224,3]
denotation: Image(RGB)
Description: Input image to be classified. The input is expected to be an RGB image with type UINT8.
identifier: 0

Outputs

name: shopping_intent
tensor: float32[1,4]
denotation: Feature
Description: Probability whether the image has shopping intent
identifier: 222
name: sensitive
tensor: float32[1,2]
denotation: Feature
Description: Probability whether the image is sensitive
identifier: 220

Labels

The model provides classifications across four shopping intent categories:

LABEL_1: shopping_intent:negative
LABEL_2: shopping_intent:apparel
LABEL_3: shopping_intent:home_decor
LABEL_4: shopping_intent:other

The model provides classifications across two sensitivity categories:

LABEL_1: sensitive:negative
LABEL_2: sensitive:positive

Model Use Cases

This model can be used for:

Determining whether an analyzed image is misclassified by shopping intent.
Identifying misclassified shopping categories.
Detecting images with ambiguous intent and category.

How It Works

The model takes in a pre-processed image (224x224) and returns two sets of probabilities:

Shopping intent (4 labels)
Sensitive image (2 labels)

Technical Specifications

Model Inputs:

name: normalized_input_image_tensor
tensor: float32[1,224,224,3]
denotation: Image(RGB)
Description: Input image to be classified. The input is expected to be an RGB image with type UINT8.
identifier: 0

Model Outputs:

name: shopping_intent
tensor: float32[1,4]
denotation: Feature
Description: Probability whether the image has shopping intent
identifier: 222
name: sensitive
tensor: float32[1,2]
denotation: Feature
Description: Probability whether the image is sensitive
identifier: 220

Model Architecture

The full model architecture is available as: PNG | SVG

Additional Information

Original Article: Link to the article

Practical Application

Interested in using this model in an automated pipeline for bulk image classification? Please book an appointment to discuss your needs.

dejanseo/shopping-intent