Update image size

app.py

@@ -29,7 +29,7 @@ def get_model():
     detection_model = AutoDetectionModel.from_pretrained(
         model_type='yolov8',
         model_path=yolov8_model_path,
-        confidence_threshold=0.75,
+        confidence_threshold=0.8,
         device="cpu",
     )
     return detection_model
@@ -93,10 +93,10 @@ with col3:
     st.markdown('##### Set model parameters')
     slice_size = st.slider(
         label = 'Slice Size', 
-        min_value=256,
-        max_value=1024, 
-        value=512, 
-        step=256
+        min_value=1240,
+        max_value=4960, 
+        value=2480, 
+        step=1240
     )
     overlap_ratio = st.slider(
         label = 'Overlap Ratio', 
@@ -124,7 +124,7 @@ if submit:
     with st.spinner(text="Downloading model weights ... "):
         detection_model = get_model()
         
-    image_size = 1280
+    image_size = 4960
 
     with st.spinner(text="Performing prediction ... "):
         output = sahi_yolov8m_inference(
@@ -152,7 +152,7 @@ with col2:
         img2=st.session_state["output_2"],
         label1='Uploaded Diagram',
         label2='Model Inference',
-        width=1024,
+        width=1240,
         starting_position=50,
         show_labels=True,
         make_responsive=True,

utils.py

@@ -9,12 +9,12 @@ TEMP_DIR = "temp"
 def sahi_yolov8m_inference(
     image,
     detection_model,
-    slice_height=512,
-    slice_width=512,
+    slice_height=1240,
+    slice_width=1240,
     overlap_height_ratio=0.1,
     overlap_width_ratio=0.1,
-    image_size=1280,
-    postprocess_match_threshold=0.75,
+    image_size=4960,
+    postprocess_match_threshold=0.8,
 ):
     # sliced inference
     detection_model.image_size = image_size