diff --git a/.riahub/workflows/workflow.yaml b/.riahub/workflows/workflow.yaml
index 623c344..d9f7c27 100644
--- a/.riahub/workflows/workflow.yaml
+++ b/.riahub/workflows/workflow.yaml
@@ -59,7 +59,7 @@ jobs:
       - name: Upload Dataset Artifacts
         uses: actions/upload-artifact@v3
         with:
-          name: ria-dataset
+          name: dataset
           path: data/dataset/**
 
 
@@ -75,7 +75,7 @@ jobs:
       - name: Upload Checkpoints
         uses: actions/upload-artifact@v3
         with:
-          name: ria-checkpoints
+          name: checkpoints
           path: checkpoint_files/inference_recognition_model.ckpt
 
       - name: 4. Convert to ONNX file
diff --git a/onnx_scripts/convert_to_onnx.py b/onnx_scripts/convert_to_onnx.py
index 4dfa4f5..0458b4f 100644
--- a/onnx_scripts/convert_to_onnx.py
+++ b/onnx_scripts/convert_to_onnx.py
@@ -3,8 +3,7 @@ import os
 import numpy as np
 import torch
 
-from data.training.mobilenetv3 import mobilenetv3, RFClassifier
-from onnx_files import ONNX_DIR
+from scripts.training.mobilenetv3 import mobilenetv3, RFClassifier
 from helpers.app_settings import get_app_settings
 
 
@@ -48,12 +47,12 @@ def convert_to_onnx(ckpt_path, fp16=False):
     # Generate random sample data
     base, ext = os.path.splitext(os.path.basename(ckpt_path))
     if fp16:
-        output_path = os.path.join(ONNX_DIR, f"{base}.onnx")
+        output_path = os.path.join("onnx_scripts", f"{base}.onnx")
         sample_input = torch.from_numpy(
             np.random.rand(batch_size, in_channels, slice_length).astype(np.float16)
         )
     else:
-        output_path = os.path.join(ONNX_DIR, f"{base}.onnx")
+        output_path = os.path.join("onnx_scripts", f"{base}.onnx")
         sample_input = torch.rand(batch_size, in_channels, slice_length)
 
     torch.onnx.export(
@@ -70,18 +69,17 @@ def convert_to_onnx(ckpt_path, fp16=False):
 
 
 if __name__ == "__main__":
-    from checkpoint_files import CHECKPOINTS_DIR
 
     settings = get_app_settings()
 
-    model_checkpoint = settings.training.checkpoint_filename + ".ckpt"
+    model_checkpoint = "inference_recognition_model.ckpt"
 
     print("Converting to ONNX...")
 
     convert_to_onnx(
-        ckpt_path=os.path.join(CHECKPOINTS_DIR, model_checkpoint), fp16=False
+        ckpt_path=os.path.join("checkpoint_files", model_checkpoint), fp16=False
     )
 
-    output_file = settings.inference.onnx_model_filename + ".onnx"
+    output_file = "convert_to_onnx.py" + ".onnx"
 
-    print("Conversion complete stored at: ", os.path.join(ONNX_DIR, output_file))
+    print("Conversion complete stored at: ", os.path.join("onnx_scripts", output_file))