Geohash Image Scoring for Rare Object Labeling
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Solution Overview
Problem
Conventional AI models face performance issues when identifying rare objects due to insufficient training data, leading to inefficient data labeling processes that are resource-intensive and time-consuming.
Innovation Solution
An image scoring system that uses metadata, such as geographic information, to prioritize and select images for labeling based on estimated frequencies of object classes, thereby improving the efficiency of data selection and model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If manual labeling of large quantities of images is performed to improve AI model performance, then the quality and quantity of training data increases, but the resource consumption and time required increase significantly
Solution Approach 1:
The system performs preliminary actions by using AI models to pre-analyze and predict the presence of target objects in images before human labelers review them. This preliminary analysis filters out images that are unlikely to contain target objects, so that human labelers only need to review a small subset of promising images, dramatically reducing the time and resources required for manual labeling while still accumulating sufficient training data.
2Quantity of substance
If manual labeling of large quantities of images is performed to improve AI model performance, then the quality and quantity of training data increases, but the resource consumption increases significantly
Solution Approach 1:
The system performs preliminary actions by using AI models to pre-analyze and predict the presence of target objects in images before human labelers review them. This preliminary analysis filters out images that are unlikely to contain target objects, so that human labelers only need to review a small subset of promising images, dramatically reducing the time and resources required for manual labeling while still accumulating sufficient training data.
Solution Approach 2:
The system introduces an intermediary AI-based filtering layer between the large corpus of unlabeled images and the human labelers. This intermediary uses trained models to predict target object presence and prioritize images for labeling, acting as a mediator that reduces the workload on human labelers while maintaining data quality, thereby improving overall labeling productivity.
3Reliability
If AI models are trained with more data to improve performance on rare objects, then the model accuracy improves, but the data selection process becomes more complex
Solution Approach 1:
The system introduces an intermediary AI-based filtering layer between the large corpus of unlabeled images and the human labelers. This intermediary uses trained models to predict target object presence and prioritize images for labeling, acting as a mediator that reduces the workload on human labelers while maintaining data quality, thereby improving overall labeling productivity.
Solution Approach 2:
The system changes the parameters of image selection by using AI model predictions of target object presence as a new selection criterion. Instead of randomly selecting or manually reviewing all images, the system transforms the selection process into one based on predicted probability scores, automatically prioritizing images with higher likelihood of containing target objects. This parameter-based approach simplifies data selection while improving the relevance and quality of training data for rare objects.
Data Source
AI summary
Systems and methods for image scoring are disclosed. For example, the method includes: receiving a plurality of images, each image of the plurality of images associated with a geographic metadata, the plurality of images associated with one or more geographic metadata; selecting a first set of images from the plurality of images, each image in the first set of images being associated with a first geographic metadata that is one of the one or more geographic metadata; obtaining one or more first labels indicating one or more object classes respectively for each image in the first set of images; and determining one or more first scores for the one or more object classes for the first geographic metadata based at least in part on the one or more first labels.