diff --git a/config-train.yaml b/config-train.yaml
index a239aa2..02004fb 100644
--- a/config-train.yaml
+++ b/config-train.yaml
@@ -5,10 +5,10 @@ output_model_path: "Zombely/plwiki-proto-fine-tuned-v3"
 wandb_test_name: "fiszki-ocr-fine-tune"
 checkpoint_path: "./checkpoint"
 max_length: 768
-image_size: [1920, 2560]
+image_size: [2560, 1920]
 train_config:
   max_epochs: 1
-  val_check_interval: 0.5 
+  val_check_interval: 0.5
   check_val_every_n_epoch: 1
   gradient_clip_val: 1.0
   num_training_samples_per_epoch: 800
diff --git a/utils/donut_dataset.py b/utils/donut_dataset.py
index 2d2a502..f66b05d 100644
--- a/utils/donut_dataset.py
+++ b/utils/donut_dataset.py
@@ -7,7 +7,6 @@ import torch
 from transformers import DonutProcessor, VisionEncoderDecoderModel
 
 
-
 class DonutDataset(Dataset):
     """
     DonutDataset which is saved in huggingface datasets format. (see details in https://huggingface.co/docs/datasets)
@@ -132,7 +131,7 @@ class DonutDataset(Dataset):
         sample = self.dataset[idx]
 
         # inputs
-        pixel_values = self.processor(sample["image"], random_padding=self.split == "train", return_tensors="pt").pixel_values
+        pixel_values = self.processor(sample["image"].convert("RGB"), random_padding=self.split == "train", return_tensors="pt").pixel_values
         pixel_values = pixel_values.squeeze()
 
         # targets
@@ -149,4 +148,4 @@ class DonutDataset(Dataset):
         labels = input_ids.clone()
         labels[labels == self.processor.tokenizer.pad_token_id] = self.ignore_id  # model doesn't need to predict pad token
         # labels[: torch.nonzero(labels == self.prompt_end_token_id).sum() + 1] = self.ignore_id  # model doesn't need to predict prompt (for VQA)
-        return pixel_values, labels, target_sequence
\ No newline at end of file
+        return pixel_values, labels, target_sequence