add yolov10

requirements.txt

@@ -0,0 +1,9 @@
+torch
+torchvision
+onnx
+onnxruntime
+pycocotools
+PyYAML
+scipy
+onnxsim
+onnxruntime-gpu

ultralytics/__init__.py

@@ -3,7 +3,7 @@
 __version__ = "8.1.34"
 
 from ultralytics.data.explorer.explorer import Explorer
-from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld
+from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld, YOLOv10
 from ultralytics.models.fastsam import FastSAM
 from ultralytics.models.nas import NAS
 from ultralytics.utils import ASSETS, SETTINGS as settings
@@ -23,4 +23,5 @@
     "download",
     "settings",
     "Explorer",
+    "YOLOv10"
 )

ultralytics/cfg/__init__.py

@@ -549,6 +549,10 @@ def entrypoint(debug=""):
         from ultralytics import SAM
 
         model = SAM(model)
+    elif "yolov10" in stem:
+        from ultralytics import YOLOv10
+
+        model = YOLOv10(model)
     else:
         from ultralytics import YOLO
 

ultralytics/cfg/models/v10/yolov10b.yaml

@@ -0,0 +1,40 @@
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  b: [0.67, 1.00, 512] 
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2fCIB, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2fCIB, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

ultralytics/cfg/models/v10/yolov10l.yaml

@@ -0,0 +1,40 @@
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2fCIB, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2fCIB, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

ultralytics/cfg/models/v10/yolov10m.yaml

@@ -0,0 +1,43 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2fCIB, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2f, [512]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

ultralytics/cfg/models/v10/yolov10n.yaml

@@ -0,0 +1,40 @@
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024] 
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2f, [512]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2f, [512]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

ultralytics/cfg/models/v10/yolov10s.yaml

@@ -0,0 +1,39 @@
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  s: [0.33, 0.50, 1024]
+
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2fCIB, [1024, True, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2f, [512]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2f, [512]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

ultralytics/cfg/models/v10/yolov10x.yaml

@@ -0,0 +1,40 @@
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  x: [1.00, 1.25, 512]
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2fCIB, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2fCIB, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2fCIB, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

ultralytics/engine/exporter.py

@@ -67,7 +67,7 @@
 from ultralytics.data.dataset import YOLODataset
 from ultralytics.data.utils import check_det_dataset
 from ultralytics.nn.autobackend import check_class_names, default_class_names
-from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder
+from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder, v10Detect
 from ultralytics.nn.tasks import DetectionModel, SegmentationModel, WorldModel
 from ultralytics.utils import (
     ARM64,
@@ -229,6 +229,9 @@ def __call__(self, model=None):
                 m.dynamic = self.args.dynamic
                 m.export = True
                 m.format = self.args.format
+                if isinstance(m, v10Detect):
+                    m.max_det = self.args.max_det
+
             elif isinstance(m, C2f) and not any((saved_model, pb, tflite, edgetpu, tfjs)):
                 # EdgeTPU does not support FlexSplitV while split provides cleaner ONNX graph
                 m.forward = m.forward_split

ultralytics/engine/trainer.py

@@ -425,7 +425,8 @@ def _do_train(self, world_size=1):
                 self.ema.update_attr(self.model, include=["yaml", "nc", "args", "names", "stride", "class_weights"])
 
                 # Validation
-                if self.args.val or final_epoch or self.stopper.possible_stop or self.stop:
+                if (self.args.val and (((epoch+1) % 10 == 0) or (self.epochs - epoch) <= 10)) \
+                    or final_epoch or self.stopper.possible_stop or self.stop:
                     self.metrics, self.fitness = self.validate()
                 self.save_metrics(metrics={**self.label_loss_items(self.tloss), **self.metrics, **self.lr})
                 self.stop |= self.stopper(epoch + 1, self.fitness) or final_epoch

ultralytics/engine/validator.py

@@ -196,10 +196,16 @@ def __call__(self, trainer=None, model=None):
         self.check_stats(stats)
         self.speed = dict(zip(self.speed.keys(), (x.t / len(self.dataloader.dataset) * 1e3 for x in dt)))
         self.finalize_metrics()
-        self.print_results()
+        # self.print_results()
         self.run_callbacks("on_val_end")
         if self.training:
             model.float()
+            assert(self.args.save_json and self.jdict)
+            with open(str(self.save_dir / "predictions.json"), "w") as f:
+                LOGGER.info(f"Saving {f.name}...")
+                json.dump(self.jdict, f)  # flatten and save
+            stats = self.eval_json(stats)  # update stats
+            stats['fitness'] = stats['metrics/mAP50-95(B)']
             results = {**stats, **trainer.label_loss_items(self.loss.cpu() / len(self.dataloader), prefix="val")}
             return {k: round(float(v), 5) for k, v in results.items()}  # return results as 5 decimal place floats
         else:

ultralytics/models/__init__.py

@@ -3,5 +3,6 @@
 from .rtdetr import RTDETR
 from .sam import SAM
 from .yolo import YOLO, YOLOWorld
+from .yolov10 import YOLOv10
 
-__all__ = "YOLO", "RTDETR", "SAM", "YOLOWorld"  # allow simpler import
+__all__ = "YOLO", "RTDETR", "SAM", "YOLOWorld", "YOLOv10"  # allow simpler import

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/__init__.py

@@ -0,0 +1,5 @@
+from .model import YOLOv10
+from .predict import YOLOv10DetectionPredictor
+from .val import YOLOv10DetectionValidator
+
+__all__ = "YOLOv10DetectionPredictor", "YOLOv10DetectionValidator", "YOLOv10"

ultralytics/models/yolov10/model.py

@@ -0,0 +1,18 @@
+from ..yolo import YOLO
+from ultralytics.nn.tasks import YOLOv10DetectionModel
+from .val import YOLOv10DetectionValidator
+from .predict import YOLOv10DetectionPredictor
+from .train import YOLOv10DetectionTrainer
+
+class YOLOv10(YOLO):
+    @property
+    def task_map(self):
+        """Map head to model, trainer, validator, and predictor classes."""
+        return {
+            "detect": {
+                "model": YOLOv10DetectionModel,
+                "trainer": YOLOv10DetectionTrainer,
+                "validator": YOLOv10DetectionValidator,
+                "predictor": YOLOv10DetectionPredictor,
+            },
+        }