release YOLOX on COCO

README.md

@@ -105,7 +105,7 @@ python train.py --cuda -d coco --root path/to/COCO -v yolov1 -bs 16 --max_epoch
 | YOLOv3       | DarkNet-53    |  640  |  √   |  250  |       |        42.9            |       63.5        | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_coco.pth) |
 | YOLOv4       | CSPDarkNet-53 |  640  |  √   |  250  |       |                        |                   |  |
 | YOLOv5       | CSPDarkNet-L  |  640  |  √   |  250  |       |                        |                   |  |
-| YOLOX        | CSPDarkNet-L  |  640  |  √   |  300  |       |        46.6            |       66.1        |  |
+| YOLOX        | CSPDarkNet-L  |  640  |  √   |  300  |       |        46.6            |       66.1        | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_coco.pth) |
 | YOLOv7-Nano  | ELANNet-Nano  |  640  |  √   |  300  |       |                        |                   |  |
 | YOLOv7-Tiny  | ELANNet-Tiny  |  640  |  √   |  300  |       |                        |                   |  |
 | YOLOv7-Large | ELANNet-Large |  640  |  √   |  300  |       |                        |                   |  |

config/yolov7_config.py

@@ -24,8 +24,8 @@ yolov7_cfg = {
         'fpn_act': 'lrelu',
         'fpn_norm': 'BN',
         'fpn_depthwise': True,
-        'nbranch': 2.0,       # number of branch in ELANBlockFPN
-        'depth': 1.0,        # depth factor of each branch in ELANBlockFPN
+        'nbranch': 2.0,        # number of branch in ELANBlockFPN
+        'depth': 1.0,          # depth factor of each branch in ELANBlockFPN
         'width': 0.25,         # width factor of channel in FPN
         # head
         'head': 'decoupled_head',
@@ -53,8 +53,8 @@ yolov7_cfg = {
         'ema_tau': 2000,
         # lr schedule
         'scheduler': 'linear',
-        'lr0': 0.01,               # SGD: 0.01;     AdamW: 0.004
-        'lrf': 0.01,               # SGD: 0.01;     AdamW: 0.05
+        'lr0': 0.01,               # SGD: 0.01;     AdamW: 0.001
+        'lrf': 0.01,               # SGD: 0.01;     AdamW: 0.01
         'warmup_momentum': 0.8,
         'warmup_bias_lr': 0.1,
     },
@@ -111,8 +111,8 @@ yolov7_cfg = {
         'ema_tau': 2000,
         # lr schedule
         'scheduler': 'linear',
-        'lr0': 0.01,               # SGD: 0.01;     AdamW: 0.004
-        'lrf': 0.01,               # SGD: 0.01;     AdamW: 0.05
+        'lr0': 0.01,               # SGD: 0.01;     AdamW: 0.001
+        'lrf': 0.01,               # SGD: 0.01;     AdamW: 0.01
         'warmup_momentum': 0.8,
         'warmup_bias_lr': 0.1,
     },
@@ -169,8 +169,8 @@ yolov7_cfg = {
         'ema_tau': 2000,
         # lr schedule
         'scheduler': 'linear',
-        'lr0': 0.01,               # SGD: 0.01;     AdamW: 0.004
-        'lrf': 0.01,               # SGD: 0.01;     AdamW: 0.05
+        'lr0': 0.01,               # SGD: 0.01;     AdamW: 0.001
+        'lrf': 0.01,               # SGD: 0.01;     AdamW: 0.01
         'warmup_momentum': 0.8,
         'warmup_bias_lr': 0.1,
     },
@@ -227,8 +227,8 @@ yolov7_cfg = {
         'ema_tau': 2000,
         # lr schedule
         'scheduler': 'linear',
-        'lr0': 0.01,               # SGD: 0.01;     AdamW: 0.004
-        'lrf': 0.01,               # SGD: 0.01;     AdamW: 0.05
+        'lr0': 0.01,               # SGD: 0.01;     AdamW: 0.001
+        'lrf': 0.01,               # SGD: 0.01;     AdamW: 0.01
         'warmup_momentum': 0.8,
         'warmup_bias_lr': 0.1,
     },