print('Build yolov5 failed!') exit(ret) print('done') # Export RKNN model print('--> Export RKNN model') ret = rknn.export_rknn(RKNN_MODEL) if ret != 0: print('Export yolov5rknn failed!') exit(ret) print('done') # init runtime environment print('--> Init runtime environment...
如果在init_runtime中添加target="rk3588"参数,会报错如下: librknn_api.so: undefined symbol: rknn_set_core_mask 如果不指定target参数,会正常运行,但是速度会慢2-3倍,请问是什么原因? Activity Sign up for freeto join this conversation on GitHub.Already have an account?Sign in to comment...
The YOLOv5-seg and YOLOv8-seg processors have been integrated into this Stream example however instance segmentation requires alot of post processing via the CPU and achieving 30 FPS on a 720p video is not possible. When either of these processors are used the code downgrades to 10 FPS. Fur...
If a Model (ie: specific YOLO version) is not yet supported, a post processor could be written to handle the outputs from the RKNN engine in the same manner the YOLOv5 code has been created.NoticeThis code is being used in production for Image Classification. Over time it will be ...
(rt) // create YOLOv5 post processor switch modelType { case "v8": d.process = postprocess.NewYOLOv8(postprocess.YOLOv8COCOParams()) case "v5": d.process = postprocess.NewYOLOv5(postprocess.YOLOv5COCOParams()) case "v10": d.process = postprocess.NewYOLOv10(postprocess...
The YOLOv5-seg and YOLOv8-seg processors have been integrated into this Stream example however instance segmentation requires alot of post processing via the CPU and achieving 30 FPS on a 720p video is not possible. When either of these processors are used the code downgrades to 10 FPS. Fur...