sections/conclusion_future_work/future_scope.md

We hope to improve this project in the future by using:
- Superior translation model: Translation has a very huge impact on how the end model would perform. Better translators (for e.g. Google Translate API) and language specific seq2seq models for translation are able to generate better data, both for high-resource and low-resource languages.
- Checking translation quality: Inspecting quality of translated data is as important as the translation model itself. For this we'll either require native speakers to manually inspect a sample of translated data or devise some unsupervised translation quality metrics for the same.
- More data: Currently we are using only 2.5M images of Conceptual 12M for image captioning. We plan to include other datasets like Conceptual Captions 3M, subset of YFCC100M dataset etc.
- Low resource languages: With better translation tools we also wish to train our model in low resource languages which would further democratize the image captioning solution and help people realise the potential of language systems.
- Accessibility: Making the model deployable on hand-held devices to make it more accessible. Currently, our model is too large to fit on mobile/edge devices because of which not many will be able to access it. However, our final goal is ensure everyone can access it without any computation barriers. Hopefully we'll be able to support TFLite for our model as well in future.
- More models: We can combine several decoders with the CLIP-Vision encoder to get multilingual mdoels. We also wish to work with Marian models for language-specific captioning models, especially for low-resource languages.
- Better training: We wish to experiment more with hyperparameters, optimizers, and learning rate schedulers to make the training work better. Our validation curve, as of now, plateaus in a very few epochs and we wish to address this issue.