update

README.md

@@ -143,10 +143,10 @@ python train.py --cuda -d coco --root path/to/COCO -m yolov1 -bs 16 --max_epoch
 
 | Model    | Scale | Batch | AP<sup>test<br>0.5:0.95 | AP<sup>test<br>0.5 | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
 |----------|-------|-------|-------------------------|--------------------|------------------------|-------------------|-------------------|--------------------|--------|
-| YOLOvx-N |  640  | 4xb32 |                         |                    |                        |                   |                   |                    |  |
-| YOLOvx-T |  640  | 4xb32 |                         |                    |                        |                   |                   |                    |  |
-| YOLOvx-S |  640  | 4xb32 |                         |                    |                        |                   |                   |                    |  |
-| YOLOvx-M |  640  | 8xb16 |                         |                    |                        |                   |                   |                    |  |
+| YOLOvx-N |  640  | 4xb32 |                         |                    |                        |                   |      9.1          |        2.4         |  |
+| YOLOvx-T |  640  | 4xb32 |                         |                    |                        |                   |      18.9         |        5.1         |  |
+| YOLOvx-S |  640  | 4xb32 |                         |                    |                        |                   |      33.6         |        9.0         |  |
+| YOLOvx-M |  640  | 8xb16 |                         |                    |                        |                   |      87.4         |        23.6        |  |
 | YOLOvx-L |  640  | 8xb16 |         50.2            |        68.6        |          50.0          |        68.4       |      176.6        |        47.6        | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolovx_l_coco.pth) |
 | YOLOvx-X |  640  |       |                         |                    |                        |                   |                   |                    |  |
 
@@ -154,6 +154,15 @@ python train.py --cuda -d coco --root path/to/COCO -m yolov1 -bs 16 --max_epoch
 - We use `YOLOv5-style Mosaic augmentation` and `YOLOX-style Mixup augmentation` wihout rotation.
 - Due to my limited computing resources, I can not train `YOLOvx-X` with the setting of `batch size=128`.
 
+#### LODet: An Empirical Study of Designing Lightweight Object Detector
+
+| Model | Scale | AP<sup>test<br>0.5:0.95 | AP<sup>test<br>0.5 | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
+|-------|-------|-------------------------|--------------------|------------------------|-------------------|-------------------|--------------------|--------|
+| LODet |  320  |                         |                    |                        |                   |      1.05         |        1.20        |  |
+| LODet |  416  |                         |                    |                        |                   |      1.78         |        1.20        |  |
+| LODet |  512  |                         |                    |                        |                   |      2.70         |        1.20        |  |
+| LODet |  640  |                         |                    |                        |                   |      4.21         |        1.20        |  |
+
 #### Redesigned RT-DETR:
 
 | Model     | Scale | Batch | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |

config/__init__.py

@@ -137,7 +137,7 @@ def build_model_config(args):
     elif args.model in ['yolox_n', 'yolox_s', 'yolox_m', 'yolox_l', 'yolox_x']:
         cfg = yolox_cfg[args.model]
     # YOLOvx
-    elif args.model in ['yolovx_n', 'yolovx_s', 'yolovx_m', 'yolovx_l', 'yolovx_x']:
+    elif args.model in ['yolovx_n', 'yolovx_t', 'yolovx_s', 'yolovx_m', 'yolovx_l', 'yolovx_x']:
         cfg = yolovx_cfg[args.model]
     # LODet
     elif args.model == 'lodet':

models/detectors/__init__.py

@@ -46,7 +46,7 @@ def build_model(args,
         model, criterion = build_yolov7(
             args, model_cfg, device, num_classes, trainable, deploy)
     # YOLOvx
-    elif args.model in ['yolovx_n', 'yolovx_s', 'yolovx_m', 'yolovx_l', 'yolovx_x']:
+    elif args.model in ['yolovx_n', 'yolovx_t', 'yolovx_s', 'yolovx_m', 'yolovx_l', 'yolovx_x']:
         model, criterion = build_yolovx(
             args, model_cfg, device, num_classes, trainable, deploy)
     # YOLOX