Comments (3)
You don't need the .wts to compile the layer plugin, but you need to load some weights if you want to create the yolov4 tensorrt engine. You can use the .wts file that we provide in Google Drive for the default yolov4 weights which you would get if you dowload them from darknet (the repo yolov4 comes from and was implemented in). Or you can use your own .wts file from your own training. If your training involved changing any of the network parameters you might have to change the values in the code as well, yes. So especially the lines that you highlighted and maybe also the yolo layer anchors if you changed them.
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If you post more details (e.g. your network config) we might be able to help you.
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@philipp-schmidt Sorry for late response. Would appreciate the help with figuring this out. Link to my .cfg file: https://drive.google.com/drive/folders/1A65yRs2Lbw7n6LYeC_mps9ZMO9gRuVlK?usp=sharing
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Related Issues (20)
- tritonclient.utils.InferenceServerException: [StatusCode.UNIMPLEMENTED] HOT 1
- yolov4-tiny model accuracy not right HOT 2
- Facing problem to create "engine" HOT 8
- C++ client produces no detections HOT 8
- multiple model instances issue HOT 15
- Feature: Import darknet weights instead of pytorch .wts HOT 1
- unexpected inference input 'data' HOT 2
- TensorRT 8 Support HOT 11
- Unexpected inference output 'detections' for model 'yolov4' HOT 1
- Dynamic batcing inference time HOT 1
- Support for TensorRT8 HOT 1
- where is test time? HOT 1
- Can I use this repo to use custom trained yolo-v4 with single class HOT 1
- mismatch in postprocess func HOT 9
- Triton Inference Server taking adding 3 seconds to get YOLOv4 Inference HOT 7
- How can I generate batch=5 engine? HOT 1
- error: creating server: Internal - failed to load all models - NVIDIA Triton Server for YOLOv4 HOT 1
- Error : ld cannot find -lcudart HOT 2
- tritonclient.utils.InferenceServerException: [StatusCode.UNAVAILABLE] failed to connect to all addresses HOT 2
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