Closed immohan6598 closed 1 week ago
Hi there, In my project I used a pre-trained RT-DETR from ultralytics so the network output layer might be different from your model. Also I used 640x640 hardcoded input, I should modify my project to make it work for any size (maybe specifying it from cli).
Which RT-DETR did you use from lyuwenyu model zoo?
What did you use to convert the binary to tensorrt engine, trtexec after onnx conversion or torch2trt?
Hi, Thanks for the answer. I used this official repo https://github.com/lyuwenyu/RT-DETR/tree/main/rtdetr_pytorch Guideline is also available to convert a model to tensor-rt
I changed the hard-coded network dimension values.
I used this mode rtdetr_r18vd
I visualized the onnx model the output. I don't have that deep understanding on infer() function that updates ouputs and shapes so just want to understand.
I assume the infer will give the final output layer data.
Thanks in Advance.
Thanks all right, I'll take a look at it as soon as I can 👍
output layer dimension
Yes is a completely different input/output format to the model I used for this project.
will you be able to support? do you work on freelancing projects? I am working with freelancers to finish projects
Yes but not too soon, I have a full-time job so I can work on my GitHub projects only in my spare time.
Sorry a bit late :smile: , I currently wrote the code to infer this model here
Hi,
Thanks for sharing a work!
I have trained a custom model using https://github.com/lyuwenyu/RT-DETR/tree/main/rtdetr_pytorch
network dimension is 1024 x 1024 and converted the .pt to tensorrt engine.
when i inference i get segmentation fault in the postprocessing
i only have 15 classes
please help me figure out this