This Streamlit application allows users to upload images and get predictions from the OpenCLIP model, an open-source implementation of OpenAI's CLIP model. The CLIP model, which stands for "Contrastive Language-Image Pre-training," is a state-of-the-art artificial intelligence model capable of understanding both images and text.
- Upload images: Users can upload images directly to the app interface.
- Get predictions: The app uses the OpenCLIP model to predict the class or content depicted in the uploaded image based on its visual features and any accompanying text description.
- Clone this repository to your local machine.
- Install the required dependencies using
pip install -r requirements.txt. - Run the Streamlit app using
streamlit run streamlit_app.py. - Once the app is running, upload an image using the provided file uploader.
- Wait for the app to process the image and display the predictions.
The OpenCLIP model is an open-source implementation of OpenAI's CLIP model, which is a powerful AI model capable of understanding both images and text. It achieves this by training on a large dataset of image-text pairs using a technique called contrastive learning. This enables the model to learn a joint representation space where images and text are semantically similar if they describe the same concept.
For more information on the original CLIP model, you can read the original paper.
The OpenCLIP implementation can be found on GitHub.
This app was created by Isaac Bravo & Katharina Prasse. It uses the OpenCLIP model, an open-source implementation of OpenAI's CLIP model.