update

ultralytics/models/yolo/detect/val.py

@@ -67,7 +67,7 @@ def init_metrics(self, model):
         val = self.data.get(self.args.split, "")  # validation path
         self.is_coco = isinstance(val, str) and "coco" in val and val.endswith(f"{os.sep}val2017.txt")  # is COCO
         self.class_map = converter.coco80_to_coco91_class() if self.is_coco else list(range(1000))
-        self.args.save_json |= self.is_coco and not self.training  # run on final val if training COCO
+        self.args.save_json |= self.is_coco  # run on final val if training COCO
         self.names = model.names
         self.nc = len(model.names)
         self.metrics.names = self.names

ultralytics/models/yolov10/predict.py

@@ -6,6 +6,9 @@
 
 class YOLOv10DetectionPredictor(DetectionPredictor):
     def postprocess(self, preds, img, orig_imgs):
+        if isinstance(preds, dict):
+            preds = preds["one2one"]
+
         if isinstance(preds, (list, tuple)):
             preds = preds[0]
 

ultralytics/models/yolov10/val.py

@@ -8,7 +8,7 @@ def __init__(self, *args, **kwargs):
         self.args.save_json |= self.is_coco
 
     def postprocess(self, preds):
-        if self.training:
+        if isinstance(preds, dict):
             preds = preds["one2one"]
 
         if isinstance(preds, (list, tuple)):

ultralytics/nn/modules/head.py

@@ -516,7 +516,7 @@ def forward(self, x):
         if not self.training:
             one2one = self.inference(one2one)
             if not self.export:
-                return one2one
+                return {"one2many": one2many, "one2one": one2one}
             else:
                 assert(self.max_det != -1)
                 boxes, scores, labels = ops.v10postprocess(one2one.permute(0, 2, 1), self.max_det)