train YOLOv7

config/model_config/yolov7_config.py

@@ -85,7 +85,7 @@ yolov7_cfg = {
         # ---------------- Train config ----------------
         ## input
         'trans_type': 'yolox_large',
-        'multi_scale': [0.5, 1.5], # 320 -> 960
+        'multi_scale': [0.5, 1.25], # 320 -> 800
         # ---------------- Assignment config ----------------
         ## matcher
         'matcher': {'center_sampling_radius': 2.5,

models/detectors/yolov7/README.md

@@ -4,7 +4,7 @@
 |-------------|---------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|
 | YOLOv7-Tiny | ELANNet-Tiny  | 8xb16 |  640  |         39.5           |       58.5        |   22.6            |   7.9              | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_tiny_coco.pth) |
 | YOLOv7      | ELANNet-Large | 1xb16 |  640  |         48.0           |       67.5        |   144.6           |   44.0             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_large_coco.pth) |
-| YOLOv7-X    | ELANNet-Huge  | 8xb8  |  640  |                        |                   |                   |                    |  |
+| YOLOv7-X    | ELANNet-Huge  |       |  640  |                        |                   |                   |                    |  |
 
 - For training, we train YOLOv7 and YOLOv7-Tiny with 300 epochs on COCO.
 - For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation, following the setting of [YOLOv5](https://github.com/ultralytics/yolov5).