Photo by Mat Hayward on Adobe Stock

Flatiron School, Full-Time Live NYC
Project Presentation Date: August 5th, 2022
Instructor: Joseph Mata

Presentation slidedeck PDF

This is a project for learning purposes. The World Wildlife Fund is not involved with this project in any way.

Elephants are crucial not only as biological and cultural icons, but as keystone species in their ecosystems. Consisting of three main species - the African Forest elephant, the African Savanna elephant, and the Asian elephant - each serves myriad purposes in their respective environments such as habitat creation, seed dispersal, forest pathway creation, and brush cover management. In 1930, an estimated 10 million wild elephants existed on the African continent. But after decades of poaching, habitat loss, and other human interventions that number declined to approximately 496,000 by 2007.

Each year, poachers kill an estimated 20,000 wild elephants according to the WWF. The vast majority of these killings are done for illegal trade, mainly consisting of the valuable tusks of the elephant - otherwise known as ivory. With so few elephants remaining, this poaching presents an existential threat to the continued survival of elephants on the planet. While great progress has been made recently, with many nations - including most consequently China in 2017 - banning the sale of ivory, there is still much work to be done. In countries such as Vietnam and Thailand, the sale of ivory is illegal in name only, with little or no enforcement and street vendors and jewelry shops alike brazenly filling their shelves with ivory products.

To combat the continued existence of the ivory trade, wildlife conservation organizations such as the WWF are tirelessly urging nations across the world to enact stricter legislation, but the emergence of online shopping and peer-to-peer marketplaces presents a challenging roadblock. Interpol's Environmental Crime Programme issued a report that revealed that millions of dollars in potentially illegal ivory are listed on online auction sites every year. A recent study by the Durrell Institute of Conservation and Ecology (DICE) in the U.K. found that the illegal ivory trade thrives on online peer-to-peer marketplaces as well.

Although on most major websites specific product listings can be searched for by keywords, there is no easy way to tell if ivory products are listed in postings under alternative names or pseudonyms. The aim of this project is to create a proof-of-concept for the WWF of a machine learning model that takes in image data of three-dimensional objects and artifacts and classifies them as being likely consisting of ivory or not. The WWF can then use this proof-of-concept as a starting point for a model that could prove beneficial in their collaboration with online marketplaces, auction sites, law enforcment agencies and wildlife NGOs to identify listings potentially containing illegal ivory products and flag them for further investigation.

Data for this project was sourced from The Metropolitan Museum of Art in New York City. The museum, which hosts a collection of nearly half a million paintings, drawings, sculptures, and other artifacts spanning over 5,000 years of history, has open-sourced data on nearly all of the items in their collection through their Collection API. The API is freely available for commercial and non-commercial use, and requires no API key to use the service.

For this project, we first searched for artifacts that have the word "ivory" listed among the object's medium(s). These artifacts formed the initial collection of ivory art pieces that we used to train our neural network. For non-ivory object images to train the neural network, we searched for objects that contained "ceramic" listed among the medium(s), as we reasoned that ceramic figures would be of a similar size and shape as most ivory objects, thus making them a difficult comparison against the ivory artifacts for the neural networks to classify.

After further exploration of the data, we discovered that many of the artifacts in our initial collection of ivory objects (all artifacts listing ivory as a medium) contained many pieces where ivory was only a minor component of the design of the artifact. In many of these cases, it was clear that ivory was such a limited aspect of the composition of the artifact that the inclusion of such objects would introduce more noise than signal. For this reason, we decided to limit our collection of ivory artifacts that we passed into the models to objects where ivory was the sole/main component.

Our highest scoring model, model 1, achieved a test accuracy score of 82.4%, an approximately 10% improvement over our baseline fully-connected neural network. Although model 1 achieved the highest test accuracy score, most of the other CNN models achieved very similar scores, so we cannot confidenly say that model 1 will consistenly outperform the others on new test data. The model performed best at classifying non-ivory objects, correctly classifying 299 out of 353 non-ivory objects (84.7%) on the test data. On ivory objects, it correctly classified 283 out of 353 artifacts (80.2%) on the test data.

While many of the ivory objects in this dataset are difficult to classify, successful classification of ivory artifacts is possible. An accuracy score of 82.4% is likely too low to be sufficient to deploy, but the fact that we were able to build a model that performed substantially better than chance shows that our proof-of-concept was successful. Additionally, due to the nature of the dataset, it would be extremely difficult to achieve very high accuracy scores on these objects. As we saw in the EDA section, some of these objects are hundreds or thousands of years old which can lead to discoloration and disfiguration of the artifacts.

Consider which forms of ivory objects the WWF wants the model to perform best at classifying, and train the model accordingly. Ivory can take many forms, shapes and colors, ranging from raw unprocessed ivory to sculpted and painted artifacts and everything in between. The more processed the ivory, the more likely it is the neural network will have a difficult time accurately classifying it.

Consider the trade-off between precision and recall. We saw that some of our models, including model 1, fared significantly better with their precision scores than their recall scores. In other words, there were far fewer instances of incorrectly classifying an artifact as ivory when in fact it was not. On the other hand, other models such as model 2 fared better on recall, having fewer instances of incorrectly classifying an artifact as non-ivory when in fact it was.

Be aware of the limitations of this and similar models. Beyond simply not achieving an especially high accuracy score, limitations exist in this model which will likely persist into other models. One of these is that the model will very likely not perform well in attempting to classify artifacts where ivory is only one of multiple components of the design, or where the ivory has been painted so as to obscure the classic white color.

Be aware that this model and similar models will also detect other forms of ivory, besides that obtained from elephants. Though the primary concern for this project was elephant ivory, we also included some other forms of ivory such as that from walrus in our data, as the physical form is likely indistinguishable in most ways so the data was still valuable. However the trade of walrus ivory is legal and is culturally and economically important to indigenous communities in the Arctic. The WWF should consider how such models can inadvertently adversely impact these communities.