How to increase YOLOS precision?
I created a custom dataset (400 images with 4 categories, split 80(train) - 20(validation)) for detecting layout elements in scanned pages (images, tables, headers, etc.). I finetuned the hustvl/yolos-base model with it using the tutorial from Niels Rogge:github.comNielsRogge/Transformers-Tutorials/blob/master/YOLOS/Fine_tuning_YOLOS_for_object_detection_on_custom_dataset_(balloon).ipynb{
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        "## Fine-tuning YOLOS on a custom dataset for object detection\n",
        "\n",This file has been truncated.show originalThe finetuning is working and the model is detecting the things i want on test images, but often the bounding box predictions are not as precise as i want them to be and the confidence for some (correctly) detected objects is very low.So what option (if any) would be promising to get better precision from the model?more training datalonger trainingchanging hyperparametersThanks for any help and suggestions in advance. I attached the loss plot for more info.image2114×497 45.8 KB