Parameterize max_det + inference default at 1000

detect.py

@@ -68,7 +68,7 @@ def detect(opt):
         pred = model(img, augment=opt.augment)[0]
 
         # Apply NMS
-        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
+        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms, max_det=opt.max_det)
         t2 = time_synchronized()
 
         # Apply Classifier
@@ -153,6 +153,7 @@ def detect(opt):
     parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
     parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
     parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
+    parser.add_argument('--max-det', type=int, default=300, help='maximum number of detections per image')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--view-img', action='store_true', help='display results')
     parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')

models/common.py

@@ -215,19 +215,21 @@ class NMS(nn.Module):
     conf = 0.25  # confidence threshold
     iou = 0.45  # IoU threshold
     classes = None  # (optional list) filter by class
+    max_det = 300 # maximum number of detections per image
 
     def __init__(self):
         super(NMS, self).__init__()
 
     def forward(self, x):
-        return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
+        return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det)
 
 
 class AutoShape(nn.Module):
     # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
     conf = 0.25  # NMS confidence threshold
     iou = 0.45  # NMS IoU threshold
     classes = None  # (optional list) filter by class
+    max_det = 300 # maximum number of detections per image
 
     def __init__(self, model):
         super(AutoShape, self).__init__()
@@ -285,7 +287,7 @@ def forward(self, imgs, size=640, augment=False, profile=False):
             t.append(time_synchronized())
 
             # Post-process
-            y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)  # NMS
+            y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det)  # NMS
             for i in range(n):
                 scale_coords(shape1, y[i][:, :4], shape0[i])
 

utils/general.py

@@ -482,7 +482,7 @@ def wh_iou(wh1, wh2):
 
 
 def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
-                        labels=()):
+                        labels=(), max_det=300):
     """Runs Non-Maximum Suppression (NMS) on inference results
 
     Returns:
@@ -498,7 +498,6 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
 
     # Settings
     min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
-    max_det = 300  # maximum number of detections per image
     max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
     time_limit = 10.0  # seconds to quit after
     redundant = True  # require redundant detections