Uncovering AI's Ability to Detect Furry Passion

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Uncovering AI's Ability to Detect Furry Passion

Table of Contents

  1. Introduction
  2. Understanding Convolutional Neural Networks
  3. The Goal of the Project
  4. Collecting Training Data
  5. Building the Initial Model
  6. Challenges Encountered
  7. Uncovering the Flawed Data Set
  8. Exploring Transfer Learning
  9. Testing the New Model
  10. Exciting Discoveries
  11. Creating a User-Friendly Website
  12. Assessing the Performance of the AI
  13. Drawbacks of the Current Model
  14. Future Improvements
  15. Conclusion

Building an Accurate Furry Detector with Convolutional Neural Networks

Have you ever wondered how artificial intelligence can be used to identify specific objects or features within images? In this article, we will explore the fascinating world of convolutional neural networks (CNNs) and delve into the creation of a furry detector using this powerful technology. So, grab your curiosity and let's dive right in!


Hi, I'm Zenith, and I'm here to introduce you to a project that I embarked on recently. Despite the initial doubts that may arise from the title and thumbnail, I assure you that this project is actually quite cool. In this video, I'll be showcasing the first project I've worked on that may have genuine utility – a furry detector powered by a convolutional neural network (CNN) image classifier. Before you get overwhelmed by the technical jargon, let me break it down for you in simpler terms.

Understanding Convolutional Neural Networks

CNNs are a type of deep learning algorithm specifically designed for image recognition tasks. They excel at detecting patterns and features within images, making them ideal for our furry detection project. While it may sound complex, understanding the basics is essential for grasping the functionality of our AI model.

The Goal of the Project

Before diving into the technicalities, it's important to understand the ultimate goal of this project. The furry detector aims to analyze uploaded images and determine whether they contain furries (people dressed in anthropomorphic animal costumes) or not. This classification allows users to filter or organize their image libraries more efficiently.

Collecting Training Data

To train our AI model, we needed a significant amount of labeled training data. Fortunately, procuring high-quality training data proved to be a relatively simple task. By utilizing an API from e621 and writing a small bot, we were able to gather images that met specific criteria: high-quality images featuring furries while avoiding content blocked by the e621 standard tags. Through this process, we accumulated an initial data set of 5,000 images to begin our training journey.

Building the Initial Model

Starting small, we developed a proof-of-concept model to gauge the feasibility of our furry detection AI. With just an hour of training, we were already astonished by the results – achieving a surprising 70% accuracy rate. However, the pursuit of perfection urged us to push the boundaries further.

Challenges Encountered

As with any ambitious project, hurdles and obstacles presented themselves along the way. One such obstacle was an unexpected plateau in our model's performance during training. The loss and accuracy values hovered consistently at the same value, indicating that something was amiss. This roadblock called for a different approach and led us to explore an alternative technique called an autoencoder.

Uncovering the Flawed Data Set

After numerous trial and error attempts, we faced a harrowing realization – our original CNNs had learned nothing. Further investigation revealed a critical flaw in our training data set – it was unbalanced. The split between not-safe-for-work (NSFW) and safe-for-work (SFW) images within the data set was 70% to 30% respectively. This skewed distribution perfectly matched our model's accuracy rate, solidifying our suspicions.

Exploring Transfer Learning

At this point, as frustration threatened to engulf us, transfer learning emerged as our last resort. Transfer learning involves leveraging an existing AI model and fine-tuning it to suit our specific needs. With our confidence at an all-time low, we turned to EfficientNet V2, a small yet powerful pre-trained AI model. Though uncertainty loomed, it was time to put this final solution to the test.

Testing the New Model

With bated breath, we initiated the training process for our AI model using EfficientNet V2. This time, we increased the scale considerably, collecting a data set of 60,000 images. The results were astounding – our accuracy soared to approximately 90.5%. Encouraged by the success, we invited people to try out our model and gained valuable insights into its capabilities.

Exciting Discoveries

Surprisingly, our furry detector exhibited proficiency in identifying not only furries but also other interesting subjects. The AI demonstrated excellent detection of large genitalia, showcasing a thorough understanding of the unique shapes and sizes within the furry community. Additionally, our model could even recognize gum, taking into account its color and consistency. These unexpected capabilities were both fascinating and rewarding.

Creating a User-Friendly Website

To make our furry detection AI accessible to the public, we developed a user-friendly website where anyone could upload an image and receive instant results. The website design aimed to simplify the process and encourage users to engage with the AI. After a few days of development and testing, we were ready to unveil our creation to the world.

Assessing the Performance of the AI

Overall, we are extremely satisfied with the performance of our AI model. Its compact size and high-performance capabilities make it compatible with various devices. However, no project is without its flaws, and our furry detector is no exception. One key drawback is the potential for improved accuracy, which could be achieved through a more finely engineered data set. Additionally, the binary nature of the model might pose challenges when attempting to detect more niche furry kinks or assessing the level of NSFW content.

Future Improvements

Looking ahead, there are several areas we hope to improve upon. Firstly, a better-balanced data set that encompasses a wider range of tags would enhance the model's accuracy and versatility. Additionally, exploring methods for detecting various levels of NSFW content could provide users with a better understanding of the content they're dealing with. Continuous refinement and enhancements will be necessary to ensure the furry detector remains relevant and useful.


In conclusion, the development of a furry detector powered by a convolutional neural network has been an exciting journey filled with challenges and triumphs. From the initial data collection to the integration of transfer learning, we have witnessed the power and potential of AI in action. We invite you to try out our furry detector on our website and share your results with us. Your feedback and insights will contribute to the ongoing improvement of this AI tool. Thank you for joining us on this adventure, and we look forward to seeing the innovative applications you discover!

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