Start export implementation (#110)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/v8/segment/predict.py

@@ -98,7 +98,7 @@ class SegmentationPredictor(DetectionPredictor):
 def predict(cfg):
     cfg.model = cfg.model or "n.pt"
     sz = cfg.imgsz
-    if type(sz) != int:  # recieved listConfig
+    if type(sz) != int:  # received listConfig
         cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]]  # expand
     else:
         cfg.imgsz = [sz, sz]

ultralytics/yolo/v8/segment/train.py

@@ -12,11 +12,9 @@ from ultralytics.yolo.utils.plotting import plot_images, plot_results
 from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
 from ultralytics.yolo.utils.torch_utils import de_parallel
 
-from ..detect import DetectionTrainer
-
 
 # BaseTrainer python usage
-class SegmentationTrainer(DetectionTrainer):
+class SegmentationTrainer(v8.detect.DetectionTrainer):
 
     def load_model(self, model_cfg=None, weights=None, verbose=True):
         model = SegmentationModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"], verbose=verbose)
@@ -174,7 +172,7 @@ class SegLoss:
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def train(cfg):
-    cfg.model = cfg.model or "models/yolov8n-seg.yaml"
+    cfg.model = cfg.model or "yolov8n-seg.yaml"
     cfg.data = cfg.data or "coco128-seg.yaml"  # or yolo.ClassificationDataset("mnist")
     trainer = SegmentationTrainer(cfg)
     trainer.train()