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.