Autotrain Meme Classification 42897109437 Open-source Model - Easily Distinguish Meme and Non-meme Images

Autotrain Meme Classification 42897109437

Developed by Hrishikesh332

This is a binary classification model trained using AutoTrain, designed to distinguish between memes and non-meme images.

Image Classification

Transformers

#High-precision classification #Content moderation #Social media analysis

Downloads 20

Release Time : 3/22/2023

Model Overview

The model can accurately classify meme and non-meme images, suitable for scenarios like content moderation.

Model Features

High accuracy

Achieves 100% accuracy, precision, recall, and F1 score on the validation set.

Efficient classification

Can quickly distinguish between meme and non-meme images.

Low carbon emissions

The training process produced only 1.1329 grams of CO2 emissions, making it environmentally friendly and efficient.

Model Capabilities

Image classification

Content recognition

Meme detection

Use Cases

Content moderation

Social media content filtering

Automatically identify memes from a large amount of user-generated content

Improves content classification efficiency

Education

Educational content filtering

Identify and filter memes in educational content

Maintains the professionalism of educational content

🚀 Meme Classification Model

This is a meme classification model that can distinguish between meme and non - meme images, which is useful for content moderation and other related tasks.

🚀 Quick Start

Dataset

The dataset consists of two types of labeled images:

Meme
Not Meme

The Meme folder contains 222 meme images, and the Not Meme folder contains 108 non - meme files. Most of the meme images have text on them, while the non - meme images cover a wide range, from sports images to various forms of text, such as in documents or image - embedded text. This diverse data is used to improve the model's accuracy and better understand memes.

Use Case

Content Moderation - The meme classification model can be used to filter meme content from the large amount of data generated in a specific domain on social media for better understanding.

Future Scope

Further work can be done on the sentiment of meme images, such as positive, violent, offensive, sarcastic, neutral, etc. This can be applied to various tasks:
- Education - To eliminate offensive content from curated memes for educational purposes.
- Brand Monitoring - To understand user sentiments through meme culture representation for the decision - making process.

✨ Features

Binary Classification: The model is trained for binary classification tasks, distinguishing between meme and non - meme images.
High Performance: As shown in the validation metrics, it has excellent performance in terms of loss, accuracy, precision, recall, AUC, and F1 - score.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Model Trained Using AutoTrain

Problem type: Binary Classification
Model ID: 42897109437
CO2 Emissions (in grams): 1.1329

Validation Metrics

Property	Details
Loss	0.025
Accuracy	1.000
Precision	1.000
Recall	1.000
AUC	1.000
F1	1.000

🔧 Technical Details

No specific technical implementation details are provided in the original document, so this section is skipped.

📄 License

No license information is provided in the original document, so this section is skipped.

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