Primary Tools
Python, FastAI, Gradio, HuggingFace
Machine Learning Computer Vision Model
Project Overview
Client
Hefner Museum of Natural History
Goal
Use AI to streamline the identification of museum specimens (mammal skulls) from photographs.
Background
The client serves as a physical repository for tens of thousands of museum specimens, including hundreds of mammal skulls. These skulls occupy a large amount of physical space and pose unique storage challenges for the client. The client requires a solution for quickly identifying the type of skull in order to streamline its association with the relevant digital metadata.
###Product Our team developed a working prototype of a computer vision model powered by AI. The model received user input (photographs of mammal skulls) and correctly classifies them into mammalian Orders.
Challenges and Solutions
TRAINING DATA SETS
The Challenge: The computer vision model (ResNet18) requires a sizeable library of training data.The Solution: Source photographs of mammal skulls from publicly available museum repositories available online, including the Smithsonian NMNH and the London NHM.
ON THE MOVE
The Challenge: The client needs a fast and agile tool that works in a collection space: often on the move and away from the office.The Solution: Design a feature-light, mobile-friendly prototype that accommodates user input directly from a mobile phone camera with minimal bloat.
DIVERSE USE CASES
The Challenge: The client has an uneven distribution of mammal skulls that span many different broad categories, but the majority fit into two large groups (artiodactyls and rodents).The Solution: Design the working prototype around these two large groups to ensure immediate utility, then expand the model and the web app to accommodate edge cases.
