Start export implementation (#110)

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

ultralytics/yolo/v8/detect/predict.py

@@ -85,7 +85,7 @@ class DetectionPredictor(BasePredictor):
 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/detect/train.py

@@ -6,8 +6,8 @@ from ultralytics.nn.tasks import DetectionModel
 from ultralytics.yolo import v8
 from ultralytics.yolo.data import build_dataloader
 from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
-from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer
-from ultralytics.yolo.utils import colorstr
+from ultralytics.yolo.engine.trainer import BaseTrainer
+from ultralytics.yolo.utils import DEFAULT_CONFIG, colorstr
 from ultralytics.yolo.utils.loss import BboxLoss
 from ultralytics.yolo.utils.ops import xywh2xyxy
 from ultralytics.yolo.utils.plotting import plot_images, plot_results
@@ -185,7 +185,7 @@ class Loss:
 
 @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.yaml"
+    cfg.model = cfg.model or "yolov8n.yaml"
     cfg.data = cfg.data or "coco128.yaml"  # or yolo.ClassificationDataset("mnist")
     # cfg.imgsz = 160
     # cfg.epochs = 5