完善 yolo7 文档

docs/YOLO模型训练.md

@@ -24,6 +24,11 @@ yolo task=segment mode=predict model=yolov8x-seg.pt source='/kaggle/input/person
 ```shell
 yolo train data=/home/jxft/datasets/hyd-action.yaml model=/home/jxft/datasets/yolov8/yolov8n.pt epochs=100 lr0=0.01
 yolo train data=/home/yiidata/datasets/hyd-action.yaml model=/home/yiidata/datasets/yolov8/yolov8n.pt epochs=10 lr0=0.01 device='0'
+
+# yolov7
+python train.py --weights=/home/yiidata/datasets/yolov7/yolov7.pt --data=/home/yiidata/datasets/hyd-action.yaml --img-size='640' --epochs=100 --batch-size=1 --device=0
+# 导出为 NMS 输出结果为 7 的 onnx 模型
+python export.py --weights runs/train/exp/weights/best.pt  --grid --end2end --simplify --max-wh 640 
 ```
 
 ## YOLO 采用代码测试数据集
@@ -111,7 +116,7 @@ RuntimeError: CUDA error: CUBLAS_STATUS_INVALID_VALUE when calling `cublasSgemm(
 Linux 系统，删除 LD_LIBRARY_PATH 环境变量即可解决。
 
 ```shell
-unset LD_LIBRARY_PATH                                                           [1]
+unset LD_LIBRARY_PATH
 ```
 
 ## 参考

docs/YOLO模型验证.md

@@ -22,6 +22,17 @@ Results saved to runs/detect/predict3
 
 ```shell
 yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128
+yolo export model=water_strean_model.pt format=onnx \
+--iou-thres 0.65 --conf-thres 0.25 --topk 100 --opset 16 \
+--sim --input-shape 1 3 640 640
+
+python export-det.py --weights water_strean_model.pt \
+--iou-thres 0.65 --conf-thres 0.25 \
+--topk 100 --opset 16 --sim --input-shape 1 3 640 640 --device "0"
+
+python export-det.py --weights water_strean_model.pt \
+--iou-thres 0.65 --conf-thres 0.25 \
+--topk 100 --opset 16 --sim --input-shape 1 3 640 640 --device cpu
 ```
 
 **注意：**