Open neixlo opened 3 years ago
Thank you for your suggestion, and it is a very interesting direction for future improvements. We will be looking forward to contributions and effort towards that.
I have committed the model converted to OpenVINO IR, although I am still in the process of trial and error and haven't tested it yet. I hope I can be of help to someone else. https://github.com/PINTO0309/PINTO_model_zoo/tree/master/085_Yolact_Edge
I have committed the model converted to OpenVINO IR, although I am still in the process of trial and error and haven't tested it yet. I hope I can be of help to someone else. https://github.com/PINTO0309/PINTO_model_zoo/tree/master/085_Yolact_Edge I have exported an yolact_edge_mobilenetv2 to OpenVINO model,and test it in c++ demo,https://drive.google.com/drive/folders/1VKgDz09vhndT6EFMEOVkI-fA7kyBf02n?usp=sharing
I have committed the model converted to OpenVINO IR, although I am still in the process of trial and error and haven't tested it yet. I hope I can be of help to someone else. https://github.com/PINTO0309/PINTO_model_zoo/tree/master/085_Yolact_Edge I have exported an yolact_edge_mobilenetv2 to OpenVINO model,and test it in c++ demo,https://drive.google.com/drive/folders/1VKgDz09vhndT6EFMEOVkI-fA7kyBf02n?usp=sharing
Thanks for sharing. How about the FPS of yolact_edge_mobilenetv2 with CPU?
I have committed the model converted to OpenVINO IR, although I am still in the process of trial and error and haven't tested it yet. I hope I can be of help to someone else. https://github.com/PINTO0309/PINTO_model_zoo/tree/master/085_Yolact_Edge I have exported an yolact_edge_mobilenetv2 to OpenVINO model,and test it in c++ demo,https://drive.google.com/drive/folders/1VKgDz09vhndT6EFMEOVkI-fA7kyBf02n?usp=sharing
Hi, thanks for the Openvino. Before getting openvino, we should convert onnx first. However, I met this problem, could you give me some suggestions. Thanks a lot.
RuntimeError: Tried to trace <torch.yolact.FPN_phase_1 object at 0x5adda0d0> but it is not part of the active trace. Modules that are called during a trace must be registered as submodules of the thing being traced.
@MiaoRain I am facing the same issue. Did you solve this issue>?
@MiaoRain I am facing the same issue. Did you solve this issue>? one way is to remove the FPN_phase_1 though changing the flow_base set in the Config file. Another one is to add the mobilenetv2 as a backbone in the Yolact model.
@MiaoRain I am facing the same issue. Did you solve this issue>? one way is to remove the FPN_phase_1 though changing the flow_base set in the Config file. Another one is to add the mobilenetv2 as a backbone in the Yolact model.
@MiaoRain could you please share your modified codes and config file with which you succeeded in exporting to onnx?
TFLite Float32, Float16, Dynamic Range Quantization, INT8, EdgeTPU, ONNX, CoreML, TFJS, TF-TRT, OpenVINO IR FP32/FP16, Myriad Inference Engine Blob. https://github.com/PINTO0309/PINTO_model_zoo/tree/main/085_Yolact_Edge/30_Full_Converted_mbnv2_550x550
Convert tool https://github.com/PINTO0309/openvino2tensorflow
Model of the source https://github.com/haotian-liu/yolact_edge/issues/7#issuecomment-766545213
@hylrh2008 Hi brother, could you please give me some guide about converting yolactedge to onnx file?
@hylrh2008 Hi brother, could you please give me some guide about converting yolactedge to onnx file?
hi, Did you convert the model successfully(*.onnx)?
All were successful. :+1: https://github.com/PINTO0309/yolact_edge_onnx_tensorrt_myriad
Dear Haotian Liu and Rafael Rivera Soto, thanks for your awesome work and your code!
I'd just want to point out a future enhancement.
Beside the TensorRT framework from Nvidia, which you are using, there is also another major hardware optimization framework: OpenVINO
OpenVINO is the hardware optimization framework for Intel CPUs, (integrated) GPUs, FPGAs and the VPUs (vision processing units). An implementation based on OpenVINO would push your work even more, due to widely used Intel hardware. Even edge devices with the Intel VPU Chips inside are on the rise and enable computing of neural nets on those devices.
Just a suggestion. But let me know if you plan something in this direction ;)