Training image captioning that too multilingual was a difficult task and we faced challenges at almost every point of this process.

- Dataset: Our initial plan was to translate Conceptual 12M using mTranslate or Yandex but they turned out to be too slow even with multiprocessing. Not having proper translation could lead to poor performance of the trained image-caption model. We translated the whole dataset using MBart50 for all languages which took around 3-4 days. Further on, we realised that mBART captions were not that good and model was not converging because of that which lead us to re-translate our captions with [Marian](https://huggingface.co/transformers/model_doc/marian.html)

- We prepared the model and config classes for our model from scratch, basing it on `CLIP model based on ViT-B/32 Image Transformer` and `mBART50` implementations in FLAX. The CLIP embeddings were to be used inside the mBART50 embeddings class, which was the major challenge here.

- RAM issues: Loading and training 10M image-caption dataset led to huge amount of RAM consumption on TPU (~200GB in the first few steps) because of which we had to optimize the script, use less data, and use less `num_workers` in order to avoid this issue. This also caused our training to slow down.

- We were only able to get around 2-3 days of training time on TPUs due to aformentioned challenges. We were unable to perform hyperparameter tuning.