{"id":2301,"date":"2024-11-14T02:27:56","date_gmt":"2024-11-14T00:27:56","guid":{"rendered":"https:\/\/www.lucaamore.com\/?p=2301"},"modified":"2024-11-20T00:37:39","modified_gmt":"2024-11-19T22:37:39","slug":"advanced-convnet-for-detecting-simpsons-characters-trained-with-transfer-learning","status":"publish","type":"post","link":"https:\/\/www.lucaamore.com\/?p=2301","title":{"rendered":"Advanced ConvNet for Detecting Simpsons Characters Trained with Transfer Learning"},"content":{"rendered":"\n

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)<\/strong> 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!<\/p>\n\n\n\n

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.<\/strong><\/p>\n\n\n\n

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Let\u2019s kick things off with a spoiler: the ConvNet we built achieved extraordinary results on every front<\/strong><\/strong>. <\/p>\n\n\n\n

The outstanding accuracy achieved in recognizing characters on any new image (not used during training) is: 93.65%<\/strong> <\/mark><\/p>\n\n\n\n

ANALYZE, REVIEW, AND FREELY MODIFY THE KAGGLE NOTEBOOK WITH THE SOURCES HERE<\/a><\/strong><\/p>\n\n\n\n

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Metrics for Validation Dataset are excellent <\/figcaption><\/figure>\n\n\n\n

How did I achieve this? I built my best CNN, trained using transfer learning<\/strong> and enhanced with data augmentation<\/strong> to improve robustness and generalization. This challenge was a magical doorway<\/strong>, plunging me once again into the fascinating world of deep learning<\/strong>\u2014a 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.<\/p>\n\n\n\n

The dataset I ventured into is the famous \u201cThe Simpsons Characters Data\u201d<\/a><\/strong> (by Alexandre Attia) available on Kaggle: 16,670 images<\/strong> covering a whopping 43 different characters from Springfield<\/strong>. After acquiring it, I stumbled upon the first challenge\u2014worthy of a Simpsons-style slip on a banana peel: the dataset is highly imbalanced<\/strong>. Main characters have thousands of images (Homer, Bart, Lisa), while others have only a few (poor Lionel Hutz and Disco Stu!).<\/p>\n\n\n\n

To balance the dataset, I had to set a threshold: only characters with at least 200 images<\/strong> 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<\/strong>.<\/p>\n\n\n\n

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Transfer learning<\/strong> is a technique where you take a pre-trained model<\/strong> (such as one trained on ImageNet<\/strong>) and \u201ctransfer\u201d its knowledge to solve a specific new task<\/strong>, like recognizing the Simpsons characters. This approach allows you to leverage the initial layers, already \u2018skilled\u2019 at recognizing general patterns<\/strong> (shapes, colors, textures), so you can focus on fine-tuning only the final layers to distinguish the unique details<\/strong> of each character. Generally, for this type of problem, it\u2019s an excellent solution<\/strong>.<\/p>\n\n\n\n

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CNN model structure<\/figcaption><\/figure>\n\n\n\n

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

Choosing ConvNet50 as the pre-trained base model<\/strong> felt like selecting the best superhero to tackle the battle for Simpsons recognition! Also known as ResNet50<\/strong>, this network is built on a 50-layer deep structure<\/strong> designed to scrutinize every pixel with microscopic precision. ConvNet50 is pre-trained on ImageNet<\/strong>, a dataset that has processed millions of images and learned to recognize even the smallest details. This means that, even if it\u2019s never seen Homer, Bart, or any Simpsons episode, it already knows how to identify shapes, colors, and textures to near perfection.<\/p>\n\n\n\n

Why ConvNet50?<\/strong> First, this network\u2019s design incorporates an architecture based on residual blocks<\/strong>. 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<\/strong>. It\u2019s like giving the network the best GPS possible, so it always knows where it\u2019s 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.<\/p>\n\n\n\n

Another base model alternatives I explored:<\/p>\n\n\n\n