I want to train an image captioning model for my language. I already have images and captions in Indonesian, but I can only find pretrained models for other languages, especially English.
Is there a code template I can use for this task? I assume image captioning follows a common structure, so having a starting point would be really helpful.
If you have all that data, most of the work is done.
All that’s left is to do the work…
I think the Course will be helpful for how to do it.
There seem to be various ways to explore things like setting hyperparameters, from manual to automatic.
and by Hugging Chat:
To train an image captioning model for Indonesian using the Hugging Face ecosystem, follow these organized steps:
Data Preparation:
Organize your dataset with images and corresponding Indonesian captions into a format compatible with the Hugging Face datasets library.
Convert images into tensor representations and tokenize Indonesian captions using an appropriate tokenizer, such as one compatible with the chosen model.
Model Selection:
Select a pre-trained image captioning model, such as BLIP, available on the Hugging Face Model Hub. This model is pre-trained on a large dataset with English captions but can be adapted.
Model Architecture Adjustment:
Utilize the existing vision encoder of the BLIP model, as it handles image processing effectively.
Modify or fine-tune the text decoder to suit the Indonesian language. Consider integrating an Indonesian language model or tokenizer for better text generation accuracy.
Tokenization Considerations:
Ensure the tokenizer is compatible with the model. If using a different tokenizer, check for compatibility issues and adjust the text decoder accordingly.
Training and Fine-Tuning:
Fine-tune the model using your Indonesian dataset. This involves retraining the text decoder while keeping the vision encoder intact, focusing on adapting the model to generate accurate Indonesian captions.
Computational Resources:
Use cloud services or Hugging Face platforms for training, as they offer the necessary computational power for processing large vision-language models.
Research and Existing Models:
Investigate existing research or pre-trained models adapted for Indonesian to leverage prior work and accelerate your project.
Evaluation and Iteration:
After training, evaluate the model’s performance. Adjust hyperparameters or the model architecture as needed based on evaluation results.
By following these steps, you can effectively adapt an English pre-trained image captioning model to generate accurate Indonesian captions, leveraging the strengths of the Hugging Face ecosystem.
thank you, this is very helpful.
But i’m still wondering on step 3. how can i modify or fine-tune the text decoder to suit the Indonesian language. thankyou