update for latest sahi changes

app.py

@@ -6,6 +6,8 @@ import sahi.slicing
 from PIL import Image
 import numpy
 
+IMAGE_SIZE = 640
+
 # Images
 sahi.utils.file.download_from_url(
     "https://user-images.githubusercontent.com/34196005/142730935-2ace3999-a47b-49bb-83e0-2bdd509f1c90.jpg",
@@ -29,7 +31,7 @@ sahi.utils.file.download_from_url(
 
 # Model
 model = sahi.model.Yolov5DetectionModel(
-    model_path="yolov5s6.pt", device="cpu", confidence_threshold=0.5
+    model_path="yolov5s6.pt", device="cpu", confidence_threshold=0.5, image_size=IMAGE_SIZE
 )
 
 
@@ -39,8 +41,7 @@ def sahi_yolo_inference(
     slice_width=512,
     overlap_height_ratio=0.2,
     overlap_width_ratio=0.2,
-    image_size=640,
-    postprocess_type="UNIONMERGE",
+    postprocess_type="GREEDYNMM",
     postprocess_match_metric="IOS",
     postprocess_match_threshold=0.5,
     postprocess_class_agnostic=False,
@@ -75,7 +76,6 @@ def sahi_yolo_inference(
     prediction_result_2 = sahi.predict.get_sliced_prediction(
         image=image,
         detection_model=model,
-        image_size=image_size,
         slice_height=slice_height,
         slice_width=slice_width,
         overlap_height_ratio=overlap_height_ratio,
@@ -103,9 +103,9 @@ inputs = [
     gr.inputs.Number(default=0.2, label="overlap_width_ratio"),
     gr.inputs.Number(default=640, label="image_size"),
     gr.inputs.Dropdown(
-        ["NMS", "UNIONMERGE"],
+        ["NMS", "GREEDYNMM"],
         type="value",
-        default="UNIONMERGE",
+        default="GREEDYNMM",
         label="postprocess_type",
     ),
     gr.inputs.Dropdown(
@@ -124,10 +124,10 @@ title = "Small Object Detection with SAHI + YOLOv5"
 description = "SAHI + YOLOv5 demo for small object detection. Upload an image or click an example image to use."
 article = "<p style='text-align: center'>SAHI is a lightweight vision library for performing large scale object detection/ instance segmentation.. <a href='https://github.com/obss/sahi'>SAHI Github</a> | <a href='https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80'>SAHI Blog</a> | <a href='https://github.com/fcakyon/yolov5-pip'>YOLOv5 Github</a> </p>"
 examples = [
-    ["apple_tree.jpg", 256, 256, 0.2, 0.2, 640, "UNIONMERGE", "IOS", 0.5, True],
-    ["highway.jpg", 256, 256, 0.2, 0.2, 640, "UNIONMERGE", "IOS", 0.5, True],
-    ["highway2.jpg", 512, 512, 0.2, 0.2, 640, "UNIONMERGE", "IOS", 0.5, True],
-    ["highway3.jpg", 512, 512, 0.2, 0.2, 640, "UNIONMERGE", "IOS", 0.5, True],
+    ["apple_tree.jpg", 256, 256, 0.2, 0.2, 640, "GREEDYNMM", "IOS", 0.5, True],
+    ["highway.jpg", 256, 256, 0.2, 0.2, 640, "GREEDYNMM", "IOS", 0.5, True],
+    ["highway2.jpg", 512, 512, 0.2, 0.2, 640, "GREEDYNMM", "IOS", 0.5, True],
+    ["highway3.jpg", 512, 512, 0.2, 0.2, 640, "GREEDYNMM", "IOS", 0.5, True],
 ]
 
 gr.Interface(