This page is dedicated to the "Image Captioning" practice project under the resources of the "Hugging Face Tutorials" in the "ABC Compute Forum" community.

Project Overview

Image captioning is a task where a model generates a textual description for an input image. This project aims to provide hands-on experience with using Hugging Face's models and libraries to implement an image captioning system.

Key Steps

  1. Environment Setup: Ensure you have Python and the necessary libraries installed, such as transformers and torch.
  2. Data Preparation: Collect a dataset of images and corresponding captions. Common datasets include MS COCO.
  3. Model Selection: Choose a pre-trained model from Hugging Face’s model hub, such as t5-small or distilroberta-base.
  4. Fine-tuning: Fine-tune the selected model on your dataset.
  5. Evaluation: Evaluate the model's performance using metrics like BLEU score.
  6. Deployment: Deploy the model to a web application or API for real-time image captioning.

Example Dataset

Here is an example of an image and its corresponding caption.

Example Image

Further Reading

For more detailed tutorials and guides, check out the following resources:

Conclusion

The image captioning practice project is a great way to get hands-on experience with natural language processing and computer vision. Happy coding! 🚀