🧠 Inspiration We wanted to explore how AI can describe visuals the way humans do. Inspired by accessibility use cases and automation in content creation, we built an image captioning tool using Python and transformers.

💡 What it does It automatically generates human-like captions for any input image using the BLIP transformer model.

🔧 How we built it Python

Hugging Face Transformers (Salesforce/blip-image-captioning-base)

PIL for image handling

🚧 Challenges we ran into Handling image formats

Installing large transformer models locally

Making captions concise and readable

🏆 Accomplishments we're proud of Successfully implemented an end-to-end AI captioning flow

Learned how vision-language models work

📚 What we learned Working with pre-trained transformer models

Basics of image preprocessing and token decoding

Handling real-world AI applications with minimal code

Built With

  • backend
  • face
  • for
  • hugging
  • imaging
  • library)
  • pil
  • python
  • pytorch
  • salesforce/blip-image-captioning-base)
  • transformers
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