instructions

app.py

@@ -764,6 +764,8 @@ with gr.Blocks(theme=gr.themes.Soft(), css=CUSTOM_CSS) as demo:
                 4.  Adjust 'Inference Steps' and 'Seed' as needed.
                 5.  Click 'Generate Training Samples' to start the process.
                 6.  The 'Generated Samples' will appear in the main gallery, with the 'Input Mask' shown below for reference.
+
+                **Notice:** This model was trained on the **2018 Data Science Bowl** dataset. If your data's characteristics differ significantly, please consider fine-tuning the model using our project on GitHub for optimal results.
             """)
             with gr.Row(variant="panel"):
                 with gr.Column(scale=1, min_width=350):
@@ -799,6 +801,8 @@ with gr.Blocks(theme=gr.themes.Soft(), css=CUSTOM_CSS) as demo:
                 3. Set the expected 'Diameter' of the cells in pixels. Set to 0 to let the model automatically estimate it.
                 4. Adjust 'Flow Threshold' and 'Cell Probability Threshold' for finer control.
                 5. Click 'Segment Cells'. The result will be shown as a dark red overlay on the original image.
+
+                **Notice:** This model was trained on the **2018 Data Science Bowl** dataset. If your data's characteristics differ significantly, please consider fine-tuning the model using our project on GitHub for optimal results.
             """)
             with gr.Row(variant="panel"):
                 with gr.Column(scale=1, min_width=350):