train YOLOv7-Large on COCO

README.md

@@ -72,7 +72,7 @@ python train.py --cuda -d voc --root path/to/VOCdevkit -v yolov1 -bs 16 --max_ep
 | YOLOv4       | CSPDarkNet-53       |  640  |  √   |  150  |       83.6        |                          |   162.7           |   61.5             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_voc.pth) |
 | YOLOv5       | CSPDarkNet-L        |  640  |  √   |  150  |       83.8        |                          |   155.6           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov5_voc.pth) |
 | YOLOX        | CSPDarkNet-L        |  640  |  √   |  150  |       84.6        |                          |   155.4           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_voc.pth) |
-| YOLOv7-Large | ELANNet-Large       |  640  |  √   |  150  |       85.5        |                          |   144.6           |   44.0             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_voc.pth) |
+| YOLOv7-Large | ELANNet-Large       |  640  |  √   |  150  |       86.0        |                          |   144.6           |   44.0             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_large_voc.pth) |
 
 *All models are trained with ImageNet pretrained weight (IP). All FLOPs are measured with a 640x640 image size on VOC2007 test. The FPS is measured with batch size 1 on 3090 GPU from the model inference to the NMS operation.*
 
@@ -163,6 +163,19 @@ python test.py -d coco \
                --show
 ```
 
+For YOLOv7, since it uses the RepConv in PaFPN, you can add `--fuse_repconv` to fuse the RepConv block.
+```Shell
+python test.py -d coco \
+               --cuda \
+               -v yolov7_large \
+               --fuse_repconv \
+               --img_size 640 \
+               --weight path/to/weight \
+               --root path/to/dataset/ \
+               --show
+```
+
+
 ## Evaluation
 ```Shell
 python eval.py -d coco-val \

README_CN.md

@@ -72,7 +72,7 @@ python train.py --cuda -d voc --root path/to/VOC -v yolov1 -bs 16 --max_epoch 15
 | YOLOv4       | CSPDarkNet-53       |  640  |  √   |  150  |       83.6        |                          |   162.7           |   61.5             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_voc.pth) |
 | YOLOv5       | CSPDarkNet-L        |  640  |  √   |  150  |       83.8        |                          |   155.6           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov5_voc.pth) |
 | YOLOX        | CSPDarkNet-L        |  640  |  √   |  150  |       84.6        |                          |   155.4           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_voc.pth) |
-| YOLOv7-Large | ELANNet-Large       |  640  |  √   |  150  |       85.5        |                          |   144.6           |   44.0             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_voc.pth) |
+| YOLOv7-Large | ELANNet-Large       |  640  |  √   |  150  |       86.0        |                          |   144.6           |   44.0             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_large_voc.pth) |
 
 *所有的模型都使用了ImageNet预训练权重（IP），所有的FLOPs都是在VOC2007 test数据集上以640x640或1280x1280的输入尺寸来测试的。FPS指标是在一张3090型号的GPU上以batch size=1的输入来测试的，请注意，测速的内容包括模型前向推理、后处理以及NMS操作。*
 
@@ -166,6 +166,20 @@ python test.py -d coco \
                --show
 ```
 
+对于YOLOv7，由于YOLOv7的PaFPN中包含了RepConv模块，因此你可以在测试命中加上`--fuse_repconv`来融合其中的RepConv:
+
+```Shell
+python test.py -d coco \
+               --cuda \
+               -v yolov7_large \
+               --fuse_repconv \
+               --img_size 640 \
+               --weight path/to/weight \
+               --root path/to/dataset/ \
+               --show
+```
+
+
 ## 验证
 使用者可以参考下面的给出的例子在相应的数据集上去验证训练好的模型，正常情况下，使用者将会看到COCO风格的AP结果输出。
 

train.sh

@@ -3,7 +3,7 @@ python train.py \
         --cuda \
         -d coco \
         --root /mnt/share/ssd2/dataset/ \
-        -m yolov7_tiny \
+        -m yolov7_large \
         -bs 16 \
         -size 640 \
         --wp_epoch 1 \