How to Build an AI vs Human Image Detector Using Streamlit & Transformers
This article provides a step-by-step guide on how to build an AI-vs-Human image detector using Streamlit, Hugging Face Transformers, PyTorch, and a pretrained deep learning model.
Why it matters
As AI-generated images become increasingly realistic, traditional detectors become less effective. This tool can help users identify AI-generated images, which is important for maintaining trust and authenticity in visual content.
Key Points
- 1The detector accepts an uploaded image, processes it using a pretrained model, and predicts whether the image is AI-generated or human-captured
- 2The detector displays the model's confidence score and works on CPU, CUDA, or Apple Silicon (MPS)
- 3The article covers environment setup, package installation, importing dependencies, selecting the compute device, and loading the model and processor
Details
The article explains how to build an AI-vs-Human image detector using a Streamlit app. The app utilizes the Hugging Face Transformers library and a pretrained deep learning model called 'Organika/sdxl-detector' to classify uploaded images as either AI-generated or human-captured. The article covers the necessary environment setup, package installation, and the key steps involved in loading the model and image processor. It also discusses how the app can leverage different compute devices, including CPU, CUDA, and Apple Silicon (MPS), to optimize performance.
No comments yet
Be the first to comment