We attempted to incorporate multiple external datasets into our experiments, spending considerable time trying to train our VIT-H model jointly with Product10k and other datasets, as well as training on other datasets and fine-tuning on Product10k. Surprisingly, despite our efforts, our current leaderboard score was achieved only by using the Product10k dataset; all other datasets resulted in a decrease in our score.
To improve our results, we utilized re-ranking for postprocessing, which gave us a marginal improvement of approximately 0.01%. Additionally, we experimented with convnext and VIT-G models, which boosted our local score by about 0.03%. However, even with the use of TensorRT, our models were unable to pass inference in 10 minutes.