v04 update

app.py

@@ -133,10 +133,16 @@ def yaml_csv(file_path, file_tag):
 
 
 # model loading
-def model_loading(model_name, device):
+def model_loading(model_name, device, opt=["refresh_yolov5"]):
 
     # load model
-    model = torch.hub.load(model_path, model_name, force_reload=True, device=device, _verbose=False)
+    model = torch.hub.load(
+        model_path, 
+        model_name, 
+        force_reload=[True if "refresh_yolov5" in opt else False][0], 
+        device=device, 
+        _verbose=False
+    )
 
     return model
 
@@ -186,6 +192,7 @@ def pil_draw(img, countdown_msg, textFont, xyxy, font_size, opt, obj_cls_index,
     return img
 
 
+# Label and bounding box color settings
 def color_set(cls_num):
     color_list = []
     for i in range(cls_num):
@@ -211,10 +218,10 @@ def yolo_det_img(img, device, model_name, infer_size, conf, iou, max_num, model_
     if model_name_tmp != model_name:
         # Model judgment to avoid repeated loading
         model_name_tmp = model_name
-        model = model_loading(model_name_tmp, device)
+        model = model_loading(model_name_tmp, device, opt)
     elif device_tmp != device:
         device_tmp = device
-        model = model_loading(model_name_tmp, device)
+        model = model_loading(model_name_tmp, device, opt)
 
     # -------------Model tuning -------------
     model.conf = conf  # NMS confidence threshold
@@ -343,10 +350,10 @@ def yolo_det_video(video, device, model_name, infer_size, conf, iou, max_num, mo
     if model_name_tmp != model_name:
         # Model judgment to avoid repeated loading
         model_name_tmp = model_name
-        model = model_loading(model_name_tmp, device)
+        model = model_loading(model_name_tmp, device, opt)
     elif device_tmp != device:
         device_tmp = device
-        model = model_loading(model_name_tmp, device)
+        model = model_loading(model_name_tmp, device, opt)
 
     # -------------Model tuning -------------
     model.conf = conf  # NMS confidence threshold
@@ -472,7 +479,7 @@ def main(args):
     inputs_iou01 = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU threshold")
     inputs_maxnum01 = gr.Number(value=max_detnum, label="Maximum number of detections")
     inputs_clsName01 = gr.CheckboxGroup(choices=model_cls_name, value=model_cls_name, type="index", label="category")
-    inputs_opt01 = gr.CheckboxGroup(choices=["label", "pdf", "json"],
+    inputs_opt01 = gr.CheckboxGroup(choices=["label", "pdf", "json", "refresh_yolov5"],
                                     value=["label", "pdf"],
                                     type="value",
                                     label="operate")
@@ -486,7 +493,7 @@ def main(args):
     inputs_iou02 = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU threshold")
     inputs_maxnum02 = gr.Number(value=max_detnum, label="Maximum number of detections")
     inputs_clsName02 = gr.CheckboxGroup(choices=model_cls_name, value=model_cls_name, type="index", label="category")
-    inputs_opt02 = gr.CheckboxGroup(choices=["label"], value=["label"], type="value", label="operate")
+    inputs_opt02 = gr.CheckboxGroup(choices=["label", "refresh_yolov5"], value=["label"], type="value", label="operate")
 
     # Input parameters
     inputs_img_list = [
@@ -536,7 +543,7 @@ def main(args):
     title = "Gradio YOLOv5 Det v0.4"
 
     # describe
-    description = "<div align='center'>Customizable target detection model, easy to install, easy to use</div>"
+    description = "Author: 曾逸夫（Zeng Yifu）, Project Address: https://gitee.com/CV_Lab/gradio_yolov5_det, Github: https://github.com/Zengyf-CVer, thanks to [Gradio](https://github.com/gradio-app/gradio) & [YOLOv5](https://github.com/ultralytics/yolov5)"
     # article="https://gitee.com/CV_Lab/gradio_yolov5_det"
 
     # example image