Advanced ConvNet for Detecting Simpsons Characters Trained with Transfer Learning

Imagine sitting on your canary-yellow couch, sipping an ice-cold Duff with Homer, as Bart throws out a few quips and Lisa tries to correct you on every topic. Now, picture this: your one and only mission is to build a convolutional neural network (CNN) that can accurately recognize each member of the Simpsons cartoon. Because, with all that yellow around, even the best deep learning models might slip up!

The mission is clear: identify Homer, Bart, Marge, and the entire cast of Springfield with high precision and robust performance, as the images are dynamic, and the characters aren’t in obvious poses or positions.

Let’s kick things off with a spoiler: the ConvNet we built achieved extraordinary results on every front.

The outstanding accuracy achieved in recognizing characters on any new image (not used during training) is: 93.65%

ANALYZE, REVIEW, AND FREELY MODIFY THE KAGGLE NOTEBOOK WITH THE SOURCES HERE

Metrics for Validation Dataset are excellent

How did I achieve this? I built my best CNN, trained using transfer learning and enhanced with data augmentation to improve robustness and generalization. This challenge was a magical doorway, plunging me once again into the fascinating world of deep learning—a parallel dimension where every pixel offers a new opportunity to experiment with and dive into cutting-edge techniques. And so, once again, after long workdays, I found myself on the metro, in the car, or even in some magical corner, studying and coding.

The dataset I ventured into is the famous “The Simpsons Characters Data” (by Alexandre Attia) available on Kaggle: 16,670 images covering a whopping 43 different characters from Springfield. After acquiring it, I stumbled upon the first challenge—worthy of a Simpsons-style slip on a banana peel: the dataset is highly imbalanced. Main characters have thousands of images (Homer, Bart, Lisa), while others have only a few (poor Lionel Hutz and Disco Stu!).

To balance the dataset, I had to set a threshold: only characters with at least 200 images were included in the training. This allowed me to focus on well-represented characters. With this selection of Springfield’s finest, the CNN had a solid foundation to start accurately recognizing the most iconic characters.


Transfer learning is a technique where you take a pre-trained model (such as one trained on ImageNet) and “transfer” its knowledge to solve a specific new task, like recognizing the Simpsons characters. This approach allows you to leverage the initial layers, already ‘skilled’ at recognizing general patterns (shapes, colors, textures), so you can focus on fine-tuning only the final layers to distinguish the unique details of each character. Generally, for this type of problem, it’s an excellent solution.

CNN model structure

Identifying the overall structure is an art akin to magic. For this project, I sought a blend of flexibility and robustness: data augmentation enhances generalization, the base model leverages the power of transfer learning, and GlobalAveragePooling2D reduces parameters while retaining essential information. The dense layer with 512 neurons captures the main features, while Dropout prevents overfitting and boosts robustness, making the model less reliant on individual neurons and more capable of generalizing to new data. The softmax output enables accurate classification among the various characters.

Choosing ConvNet50 as the pre-trained base model felt like selecting the best superhero to tackle the battle for Simpsons recognition! Also known as ResNet50, this network is built on a 50-layer deep structure designed to scrutinize every pixel with microscopic precision. ConvNet50 is pre-trained on ImageNet, a dataset that has processed millions of images and learned to recognize even the smallest details. This means that, even if it’s never seen Homer, Bart, or any Simpsons episode, it already knows how to identify shapes, colors, and textures to near perfection.

Why ConvNet50? First, this network’s design incorporates an architecture based on residual blocks. These blocks help maintain the information flow even in a deep network like this one, ensuring high accuracy and reducing the risk of vanishing gradients. It’s like giving the network the best GPS possible, so it always knows where it’s heading, no matter how long the journey. In our case, this means a network that stays on track, avoiding drift into errors and maintaining focus even on the finest details.

Another base model alternatives I explored:

  • InceptionV3: Another deep learning powerhouse, known for its ability to handle multiple scales within images. InceptionV3 could have offered greater flexibility in recognizing details like Marge’s big blue hair or Bart’s rebellious spikes, as it explores different dimensions of the same image. Often oversized, InceptionV3 is ideal for scenarios with highly intricate details that require multi-scale detection.
  • VGG16 or VGG19: Widely used and simpler in structure compared to ConvNet50, yet deep and precise. These networks perform excellently in many computer vision applications, though they tend to be less efficient in terms of resources.
  • MobileNet: Ideal if my goal were to deploy the model on mobile or low-resource devices. MobileNet is fast and lightweight—an extreme nerd choice if I wanted my CNN to run on an embedded system. But for the Simpsons, opting for a more powerful model made sense!

Ultimately, ConvNet50 offers the perfect balance: it’s deep enough to capture the complexities of Springfield’s characters, well-trained to capture even the smallest details, and optimized to provide fast and efficient results without overloading the system.

To achieve excellent results, I incorporated data augmentation, an essential technique for improving model performance, especially when the dataset is imbalanced or limited in the number of images. In our case, data augmentation allows us to virtually increase the size of the dataset by creating variations of existing images (through rotation, zoom, contrast, and horizontal reflection).

Mad Self Portrait of myself analyzing the results archived
A self-portrait of myself analyzing the outstanding results achieved

After transfer learning, where we unlock and retrain only the final layers of the pre-trained model, I applied fine-tuning, unlocking and retraining only the last layers of the model. This approach allows the model to specialize in the details of the Simpsons characters, retaining general knowledge while adapting to the specific nuances of the new dataset.

Validation e Training Dataset – Loss and Accuracy during Transfer Training
Validation e Training Dataset - Loss and Accuracy during Fine Tuning Training
Validation e Training Dataset – Loss and Accuracy during Fine Tuning Training
Simpson Characters Recognition – Confusion Matrix

We present the simulation on images from the dataset not used in training, showcasing the actual character, the recognized character, and the network’s confidence level. These are images the network has never seen before, yet it identifies them with a high degree of certainty. Click on the images to view them in detail.

ANALYZE, REVIEW, AND FREELY MODIFY THE KAGGLE NOTEBOOK WITH THE SOURCES HERE

This project is highly reusable for numerous similar applications. Depending on the project’s requirements, one could adjust the model’s structure or choose a different base model.

GITHUB SOURCE – CNN-SIMPSON

“In the wise words of Homer Simpson, ‘Mmm… data!’ With this model, even Springfield would be proud of our machine learning journey!”

The charts and model performance metrics are based on a single execution of the notebook. With each run, these values may vary slightly, yet they consistently remain optimal and align closely with the results shown.

If this topic has sparked your interest and you’d like to delve deeper, I suggest these references:

Kaggle – Convolutional Neural Network (CNN) Tutorial – Kaggle

Coursera – Convolutional Neural Networks by Andrew Ng

Stanford University – CS231n: Convolutional Neural Networks for Visual Recognition

GitHub – CNN Explainer

Udacity – Convolutional Neural Networks

Dive into Deep Learning (D2L)

Kaggle – Transfer Learning for CV Guide

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Imaginary Journey to the Frontier of AI: A Glimpse into the Year 2912

In this imaginative journey to the frontier of AI, we’ll project ourselves into the year 2912, where we will challenge the Data Analysis module of ChatGPT v4 to solve a space-time anomaly.

interstellar titanic ship

To break the ice, I asked ChatGPT to briefly describe, based on its knowledge, how it imagines humanity in the year 2912, especially in relation to intergalactic travel.

“By the year 2912, I envision humanity as a highly advanced civilization, spread across multiple planets and star systems. Thanks to discoveries in physics and bioengineering, humans are able to adapt to different space environments and have developed intergalactic travel technology that allows crossing vast cosmic distances in a short time. Spacecrafts are elegant and sophisticated, equipped with AI systems to assist and protect travelers. Human cultures are diverse, influenced by various space colonies, yet united by a common quest for exploration and knowledge.”

I share ChatGPT’s optimism, which doesn’t describe a dystopian future or conflicts, but collaboration among civilizations; probably a reflection of its algorithms’ optimism bias.

Now, let’s describe our intergalactic problem presented on Kaggle:

Addison Howard, Ashley Chow, Ryan Holbrook. (2022). Spaceship Titanic. Kaggle

The interstellar spaceship Titanic, on its maiden voyage, is transporting about 13,000 emigrants from our solar system to three newly habitable exoplanets. Passengers come from Earth, Mars, and Europa, some in cryogenic sleep. The ship offers various services, including VIP treatments, a SPA, a shopping center, and a high-tech Virtual Reality deck. All onboard activities are tracked by a next-generation computer system.

Near Alpha Centauri, en route to the torrid 55 Cancri E, the ship encounters a space-time anomaly hidden by a dust cloud. After 1,000 years, it seems the tragedy of the previous Titanic is repeating in space.

After the impact, although the ship remains miraculously intact, nearly half the passengers are teleported to an alternate dimension!

view of spaceship

To aid rescue teams and recover the missing passengers, we must predict which passengers have been teleported using data from the damaged computer system.

Here’s where we come in with the assistance of ChatGPT v4 and its Data Analytics module.

We face a synthetic problem, created in the lab, which I consider extremely valid and complete to delve into various themes related to data analysis and machine learning algorithms.

To effectively solve these types of problems typically requires complex theoretical and programming knowledge. ChatGPT assists the operator by guiding through all stages of the process.

Here are the details of the Spaceship Titanic problem:

We start with a training file containing data on two-thirds of the passengers and information on whether they have been teleported, and a test file with the same data but no information on the remaining passengers’ teleportation status.

The challenge is to predict, as accurately as possible, whether the passengers in the test file will be teleported.

Here are the known passenger data.

CAMPODESCRIZIONE
PassengerIdA unique ID for each passenger, in the format gggg_pp where gggg indicates a group the passenger belongs to and pp is the passenger number within the group.
HomePlanetThe planet the passenger departed from, usually their permanent residence planet.
CryoSleepIndicates whether the passenger chose to be put in suspended animation for the journey. Cryosleep passengers are confined to their cabins.
CabinThe cabin number where the passenger is located. Presented in the format deck/number/side, where the side can be P for Port (left) or S for Starboard (right).
DestinationThe planet where the passenger disembarks.
AgeThe passenger’s age.
VIPIndicates whether the passenger paid for a special VIP service during the trip.
RoomServiceAmount the passenger charged to room service. NameThe names and surnames of the passenger.TransportedIndicates whether the passenger has been transported to another dimension. This is the target variable, the column we are trying to predict.

FoodCourtAmount the passenger charged to the food court.
ShoppingMallAmount the passenger charged to the shopping center.
SpaAmount the passenger charged to the spa.
VRDeckVRDeckAmount the passenger charged to the virtual reality deck.
NameThe name and surname of the passenger.
TransportedIndicates whether the passenger has been transported to another dimension. This is the target variable, the column we are trying to predict.
known passenger data

I attempted to solve the problem using ChatGPT v. 4.0 utilizing its Data Analysis module.

After presenting the training file, ChatGPT reprocessed it in the best way and also proposed the best strategies for managing missing data.

Spoiler Alert if you want to tackle this problem without any information.

I asked ChatGPT to explain how these variables might influence the Target Transported variable, and here’s the response:

CHATGPT RESPONSE — SPOILER ALERT —

Predicting whether a passenger has been teleported or not is based on analyzing various factors (or variables) in the dataset. Here’s a brief summary of how some of these variables might influence the probability of being teleported:

HomePlanet (Planet of Origin): The probability of being teleported may vary depending on the passenger’s planet of origin. There could be specific trends or policies associated with each planet influencing this probability.

CryoSleep (Cryogenic Sleep): Passengers in cryogenic sleep may have a higher probability of being teleported, perhaps due to specific procedures or protocols related to their state.

Cabin (Cabin): The type or location of the cabin might correlate with the probability of being teleported, perhaps due to proximity to specific teleportation devices or areas of the spacecraft.

Destination (Destination): The final destination might influence the probability of being teleported, perhaps due to different procedures for different places or travel distances.

Age (Age) and VIP: Though with a weaker correlation, age and VIP status may play a role, potentially due to policies or preferential treatments for certain age groups or VIPs.

Expenses (RoomService, FoodCourt, etc.): Expenditures in various categories (like RoomService, FoodCourt, etc.) show a correlation, suggesting that a passenger’s spending behavior might be linked to their probability of being teleported.

It’s important to note that these are correlations observed in the data and do not necessarily imply a direct cause-effect relationship. Teleportation decisions might be influenced by a complex interplay of these factors and others not captured in the dataset.

END OF CHATGPT RESPONSE — END OF SPOILER ALERT —

The analysis phase was truly impressive, considering it was completely automated.

chatgpt-data-analysis

During the model construction phase, ChatGPT guided us through various stages. Errors occurred several times, which were analyzed and recovered more or less effectively. A major limitation is that more complex computations are also interrupted due to timeout.

ChatGPT suggested using the Random Forest algorithm for the model.

I asked for the rationale behind this choice.

CHATGPT RESPONSE

I chose the Random Forest model to predict the teleportation status for various reasons, making it particularly suitable for this type of problem:

Handling Categorical and Continuous Variables: Random Forest effectively manages both categorical and continuous variables. In the provided dataset, we have both types of variables, making Random Forest an appropriate choice.

Robustness to Noisy Data and Outliers: Random Forest is known for its robustness to noisy data and outliers, which can be present in real datasets like this.

Lower Risk of Overfitting: Thanks to its ensemble algorithm nature, combining the results of many decision trees, Random Forest tends to have a lower risk of overfitting compared to single decision trees.

Variable Importance: Random Forest provides useful insights into variable importance, which can help understand which factors most significantly influence the prediction. This is useful for interpreting the model’s results in relation to the business problem.

Good Overall Performance: Generally, Random Forest is known for its good performance across a wide range of classification problems, making it a reliable starting point for many machine learning applications.

Flexibility and Scalability: It’s a flexible model that can be easily adapted to improve performance (e.g., adjusting the number of trees) and scales well with large datasets.

In summary, I chose Random Forest for its robustness, reliability, and ability to provide high-quality results on a complex dataset like the one provided.

END OF CHATGPT RESPONSE

The result achieved was an accuracy of 0.79822, which I find astonishing given that it was conducted in a fully guided mode; we also placed reasonably well on the leaderboard for this problem.

These are the parameters obtained on the validation set.

PARAMETERValue
Accuracy0.7901
Precision (False)0.8000
Precision (True)0.7800
Recall (False)0.7700
Recall (True)0.8100
F1-Score (False)0.7800
F1-Score (True)0.8000
validation set parameters

All the Python code created can be viewed, which is extremely useful for beginners.

ChatGPT V4 Data Analysis proved to be an incredible assistant, albeit a bit unstable as there were errors that forced me to start over.

All images were created by DALL·E 2 through ChatGPT v4.

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