Can AI Detect Furry ████?

Zenith
14 Aug 202108:02

TLDRIn this video, the creator introduces a project using a convolutional neural network to classify images, specifically to detect furries. After overcoming challenges with training data and model accuracy, the creator achieves a 90.5% accuracy rate. The AI can identify various furry features and even distinguish between safe and not safe for work content. A website is launched for users to test the model, highlighting its potential and limitations.

Takeaways

  • 🚀 The video introduces a project using a convolutional neural network (CNN) for image classification, specifically to detect furries in images.
  • 📈 The creator, Zenith, shares their journey of developing the AI, including the challenges faced and the learning process.
  • 🛠️ The AI was trained with a dataset of 5000 images, focusing on high quality and relevant content to the task.
  • 🎯 The initial model achieved a 70% accuracy, which was later improved by refining the dataset and model architecture.
  • 🔄 The project encountered issues with an unbalanced dataset, which was addressed by adjusting the distribution of different content types.
  • 🤖 Transfer learning was utilized, employing the efficientnet v2 model to enhance the AI's performance.
  • 🌟 The final model achieved an impressive 90.5% accuracy after extensive training with 60,000 images.
  • 🔍 The AI can detect specific features such as the unique shapes and sizes of furry anatomy.
  • 💡 The AI also unexpectedly learned to identify gum, based on its color and consistency.
  • 🌐 A website was created for users to test the AI model by uploading images and receiving results.
  • 📊 The creator invites viewers to share their experiences and results with the AI model on social media platforms.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is the development and demonstration of a convolutional neural network (CNN) for detecting furries in images.

  • What does the acronym 'YFAI' stand for?

    -The acronym 'YFAI' stands for 'You Furry AI', which is the name of the project the presenter is working on.

  • How does the AI determine if an image contains the desired subject?

    -The AI uses a convolutional neural network image classifier to identify relevant features and patterns in the image, thus determining if the target subject, in this case, furries, is present.

  • What was the initial accuracy of the small model with the first data set?

    -The initial accuracy of the small model with the first data set was 70%.

  • What issue did the presenter encounter when training the larger model?

    -The presenter encountered an issue where the loss and accuracy values remained the same over multiple iterations, indicating that the model was not learning properly.

  • How did the presenter address the issue of an unbalanced data set?

    -The presenter addressed the issue by creating a new data set with an equal distribution of 'safe for work' and 'not safe for work' images, which resulted in a 50% accuracy, confirming the problem.

  • What is transfer learning in the context of this project?

    -Transfer learning is the process of using a pre-existing AI model, like efficientnet v2, and retraining it to suit the specific needs of the project at hand, which in this case was to improve the performance of the furry detector.

  • What was the final accuracy of the model after further training?

    -The final accuracy of the model after further training with more images was approximately 90.5%.

  • What are some unique capabilities of the developed AI model?

    -The developed AI model can detect unique shapes and sizes of furry genitalia and even identify the right color and consistency of gum in images.

  • How can viewers test the AI model themselves?

    -Viewers can test the AI model by visiting the presenter's website, uploading an image, and receiving a result, which they can then share on Twitter.

  • What are the main limitations of the AI model as it currently stands?

    -The main limitations are that the accuracy could potentially be improved, and the model currently provides a binary outcome, which may not effectively handle more nuanced or questionable content.

Outlines

00:00

🤖 Introduction to the AI Project

The speaker, Zenith, introduces the project they are working on, which involves creating an AI using a convolutional neural network image classifier. The AI is designed to identify specific features and patterns within images, with the speaker drawing parallels to a previous furry detector project. Despite the simplicity of the concept, the development process was challenging and time-consuming, taking about a month and a half to complete. The speaker shares their journey from the initial idea in July, through the process of gathering training data, to the initial success with a small model and the subsequent challenges faced when scaling up the project.

05:20

🚀 Overcoming Challenges and Achieving Success

The speaker discusses the challenges they encountered when training a larger model with more data. They found that the model was not improving, with the loss and accuracy values stagnating at .6864, indicating an issue. After realizing that the original data sets were unbalanced, the speaker retrained the model with a more balanced dataset but achieved only 50% accuracy, leading to the realization that the original models had not learned properly. As a last resort, the speaker used transfer learning with EfficientNet v2, a small but powerful AI model. The final result was a highly accurate AI, with the speaker training it to achieve 90.5% accuracy. The speaker also created a website where users can test the AI by uploading images and sharing their results.

Mindmap

Keywords

💡Convolutional Neural Network (CNN)

A Convolutional Neural Network, often abbreviated as CNN, is a type of artificial neural network commonly used in image recognition and classification. In the context of the video, the creator is using a CNN to develop an image classifier that can identify whether an image contains furries or not. The network achieves this by learning to recognize specific features and patterns within the images it is trained on. For example, the script mentions that the AI takes an image, finds the relevant features and patterns, and determines the presence of the desired subject, which in this case is furries.

💡Image Classifier

An image classifier is a system that uses machine learning techniques to categorize images into different classes or groups based on their visual content. In the video, the creator is building an image classifier that focuses on identifying furries in images. The AI is trained to recognize specific visual elements associated with furries, which allows it to classify images accordingly. The script highlights the process of training the AI with a large dataset of images, which helps the classifier improve its accuracy in identifying the target subject.

💡Furry

In the context of the video, 'furry' refers to characters with anthropomorphic features, such as animals with human-like characteristics. The creator is interested in developing an AI that can detect the presence of such characters in images. This term is central to the video's theme as the entire project revolves around creating an AI model capable of identifying and classifying images of furries accurately.

💡API

API, or Application Programming Interface, is a set of protocols and tools that allows different software applications to communicate with each other. In the video, the creator uses the e621 API to gather images for training the AI model. This API provides access to a large database of images, which is crucial for the AI to learn and improve its image classification capabilities.

💡Machine Learning

Machine learning is a subset of artificial intelligence that involves the development of algorithms and models that allow computers to learn from and make predictions or decisions based on data. In the video, the creator discusses their journey into machine learning as they work on developing the AI to detect furries in images. The process involves training the AI with a dataset of images and refining the model to improve its accuracy and performance.

💡Training Data

Training data refers to the dataset used to teach a machine learning model how to recognize patterns, features, or behaviors. In the context of the video, the creator collects a large number of images of furries and non-furry content to serve as the training data for their AI model. The quality and diversity of the training data are essential for the AI to learn effectively and accurately classify images.

💡Accuracy

Accuracy in machine learning is a measure of how often a model's predictions or classifications are correct. In the video, the creator is focused on improving the accuracy of their AI model in detecting furries in images. They mention achieving an accuracy of 70% initially and later improving it to 90.5%, which indicates the model's ability to correctly identify the presence of furries in the images it is shown.

💡Transfer Learning

Transfer learning is a technique in machine learning where a pre-trained model is used as a starting point to develop a new model for a different but related task. In the video, the creator uses transfer learning by employing a pre-existing AI model called efficientnet v2 to improve their own model's performance. This approach allowed them to leverage the knowledge from the pre-trained model to enhance the accuracy and capabilities of their furry detector.

💡Dataset Imbalance

A dataset imbalance occurs when the number of samples in different classes within the dataset is not equal or proportional. In the video, the creator initially experiences an issue with dataset imbalance, where the majority of the images were classified as 'not safe for work,' leading to poor performance in detecting furries accurately. By adjusting the dataset to have a more balanced distribution of 'safe for work' and 'not safe for work' images, the creator was able to improve the model's accuracy.

💡Binary Classification

Binary classification is a type of machine learning problem where the goal is to classify instances into two distinct categories or classes. In the video, the AI model developed by the creator is designed for binary classification, where it must determine whether an image is either 'safe for work' or 'not safe for work' based on the presence of furries. The script mentions that the model could be improved by refining the dataset to better handle more nuanced classifications beyond this binary distinction.

💡Website

In the context of the video, the creator has developed a website where users can upload images to be classified by the AI model they trained. This interactive platform allows users to test the AI's ability to detect furries in images and provides real-time results. The website serves as a practical application of the AI model, allowing the creator to gather feedback and further improve the system based on user interactions.

Highlights

The video introduces a project using a convolutional neural network image classifier for a unique purpose.

The AI is designed to identify specific features and patterns within images, with a focus on detecting furries.

The creator, Zenith, spent a significant amount of time, over a month and a half, developing and perfecting the AI system.

A key challenge was gathering high-quality, relevant training data for the AI to learn from, using the e621 API.

The initial model achieved an impressive 70% accuracy with a small dataset of 5000 images.

In pursuit of higher accuracy, Zenith moved to a larger model and significantly increased the training dataset.

An unexpected issue arose during training where the model's loss and accuracy values remained stagnant at .6864.

The realization that the original datasets were unbalanced led to a reevaluation and adjustment of the training data.

Transfer learning with the efficientnet v2 model was the final solution applied to improve the AI's performance.

The final model achieved an outstanding 90.5% accuracy after extensive training with 60,000 images.

The AI has learned to detect specific furry characteristics, such as unique genital shapes and sizes.

An unexpected capability of the AI is its ability to detect gum and distinguish it from other substances based on color and consistency.

The AI's creator released a website where users can upload images to test the model and share their results on social media.

The AI is compact and can be run on almost any device, though there is room for improvement in terms of accuracy and binary classification.

The creator acknowledges the potential for the AI to be confused by more niche furry kinks if not trained on a diverse range of images.

The video concludes with an invitation for viewers to test the AI model and share their experiences in the comment section.

Casual Browsing

AI Generated Furry Art!? Can It Be Used To Make Furry Art Commissions?

2024-04-13 12:15:01

Quillbot vs Turnitin. Can Turnitin detect plagiarized work if it is paraphrased by Quillbot?

2024-04-22 14:35:00

Flirting with furry AI 🫢

2024-04-13 12:15:01

"AI Art" vs. The Furry Fandom

2024-04-13 12:25:01

THE FURRY FURSONA GENERATOR

2024-04-13 12:40:01

Exposing Furry SCAMMERS on Etsy

2024-04-13 12:15:01