Update app.py

app.py

@@ -29,7 +29,7 @@ model = YOLOTraining(model)
 model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False)
 model.eval()
 
-def yolo_predict(image: np.ndarray, iou_thresh: float = 0.75, thresh: float = 0.75):
+def yolo_predict(image: np.ndarray, iou_thresh: float = 0.5, thresh: float = 0.5):
 
     transforms =  A.Compose(
     [
@@ -115,21 +115,21 @@ demo = gr.Interface(
     fn=yolo_predict,
     inputs=[
         gr.Image(shape=(config.IMAGE_SIZE,config.IMAGE_SIZE), label="Input Image"),
-        gr.Slider(0, 1, value=0.75, step=0.05, label="IOU Threshold"),
-        gr.Slider(0, 1, value=0.75, step=0.05, label="Threshold")
+        gr.Slider(0, 1, value=0.5, step=0.05, label="IOU Threshold"),
+        gr.Slider(0, 1, value=0.5, step=0.05, label="Threshold")
     ],
     outputs=gr.Gallery(rows=1, columns=1),
     examples=[
-       ["examples/000001.jpg", 0.75, 0.75],
-       ["examples/000002.jpg", 0.75, 0.75],
-       ["examples/000003.jpg", 0.75, 0.75],
-       ["examples/000004.jpg", 0.75, 0.75],
-       ["examples/000005.jpg", 0.75, 0.75],
-       ["examples/000006.jpg", 0.75, 0.75],
-       ["examples/000007.jpg", 0.75, 0.75],
-       ["examples/000008.jpg", 0.75, 0.75],
-       ["examples/000009.jpg", 0.75, 0.75],
-       ["examples/000010.jpg", 0.75, 0.75]
+       ["examples/000001.jpg", 0.5, 0.5],
+       ["examples/000002.jpg", 0.5, 0.5],
+       ["examples/000003.jpg", 0.5, 0.5],
+       ["examples/000004.jpg", 0.5, 0.5],
+       ["examples/000005.jpg", 0.5, 0.5],
+       ["examples/000006.jpg", 0.5, 0.5],
+       ["examples/000007.jpg", 0.5, 0.5],
+       ["examples/000008.jpg", 0.5, 0.5],
+       ["examples/000009.jpg", 0.5, 0.5],
+       ["examples/000010.jpg", 0.5, 0.5]
        ]
 )