添加状态分类和液面分割

ultralytics_yolov8-main/RKOPT_README.md

@@ -0,0 +1,39 @@
+# RKNN optimization for exporting model
+
+## Source
+Base on https://github.com/ultralytics/ultralytics with commit id as 0b0bc56675997fe66b13aa0d250b777c8a467e32
+
+
+
+
+## What different
+With inference result values unchanged, the following optimizations were applied:
+- Change output node, remove post-process from the model. (post-process block in model is unfriendly for quantization)
+- Remove dfl structure at the end of the model. (which slowdown the inference speed on NPU device)
+- Add a score-sum output branch to speedup post-process.
+
+All the removed operation will be done on CPU. (the CPU post-process could be found in **RKNN_Model_Zoo**)
+
+
+
+
+## Export ONNX model
+
+After meeting the environment requirements specified in "./requirements.txt," execute the following command to export the model (support detect/segment/pose/obb model):
+
+```
+# Adjust the model file path in "./ultralytics/cfg/default.yaml" (default is yolov8n.pt). If you trained your own model, please provide the corresponding path. 
+# For example, filled with yolov8n.pt for detection model.
+# Filling with yolov8n-seg.pt for segmentation model.
+
+export PYTHONPATH=./
+python ./ultralytics/engine/exporter.py
+
+# Upon completion, the ".onnx" model will be generated. If the original model is "yolov8n.pt," the generated model will be "yolov8n.onnx"
+```
+
+
+
+## Convert to RKNN model, Python demo, C demo
+
+Please refer to https://github.com/airockchip/rknn_model_zoo.