add RT-DETR

config/__init__.py

@@ -1,5 +1,5 @@
 # ------------------------ Dataset Config ------------------------
-from .dataset_config import dataset_cfg
+from .data_config.dataset_config import dataset_cfg
 
 
 def build_dataset_config(args):
@@ -15,7 +15,7 @@ def build_dataset_config(args):
 
 
 # ------------------ Transform Config ----------------------
-from .transform_config import (
+from .data_config.transform_config import (
     # YOLOv5-Style
     yolov5_pico_trans_config,
     yolov5_nano_trans_config,
@@ -74,15 +74,15 @@ def build_trans_config(trans_config='ssd'):
 
 
 # ------------------ Model Config ----------------------
-from .yolov1_config import yolov1_cfg
-from .yolov2_config import yolov2_cfg
-from .yolov3_config import yolov3_cfg
-from .yolov4_config import yolov4_cfg
-from .yolov5_config import yolov5_cfg
-from .yolov7_config import yolov7_cfg
-from .yolox_config import yolox_cfg
-from .yolox2_config import yolox2_cfg
-from .rtdetr_config import rtdetr_cfg
+from .model_config.yolov1_config import yolov1_cfg
+from .model_config.yolov2_config import yolov2_cfg
+from .model_config.yolov3_config import yolov3_cfg
+from .model_config.yolov4_config import yolov4_cfg
+from .model_config.yolov5_config import yolov5_cfg
+from .model_config.yolov7_config import yolov7_cfg
+from .model_config.yolox_config import yolox_cfg
+from .model_config.yolox2_config import yolox2_cfg
+from .model_config.rtdetr_config import rtdetr_cfg
 
 
 def build_model_config(args):

train.py

@@ -153,7 +153,7 @@ def train():
     # Build Trainer
     trainer = build_trainer(args, data_cfg, model_cfg, trans_cfg, device, model_without_ddp, criterion)
 
-    # --------------------------------- Start ---------------------------------
+    # --------------------------------- Train: Start ---------------------------------
     ## Eval before training
     if args.eval_first and distributed_utils.is_main_process():
         # to check whether the evaluator can work
@@ -162,7 +162,7 @@ def train():
 
     ## Satrt Training
     trainer.train(model)
-    # --------------------------------- End ---------------------------------
+    # --------------------------------- Train: End ---------------------------------
 
     # Empty cache after train loop
     del trainer