Comments (2)
Great idea.
Converting SegNeXt into ONNX to speed up it is a good idea. However, we do not do it due to all kind of DDL. Thus, we do not know what will happen in the process
If you are interested in it, you can have a try. We are willing to provide help. You can keep this issue until the conversion is successful.
Best,
Menghao
from segnext.
Hi, I'm sorry to re-open this issue. Have you successfully deployed SegNeXt to tensorrt model?
from segnext.
Related Issues (20)
- when I set the "--gpu-id", default=2, I meet this problem. only "--gpu-id", default=0. I can train the model.
- Random seeds variance HOT 1
- MACs, not FLOPs HOT 1
- The paddlepaddle version of SegNeXt
- Welcome update to OpenMMLab 2.0
- Inconsistencies in ADE20K model inference output for the same input image. HOT 3
- How to start training? HOT 1
- AssertionError: train_cfg specified in both outer field and model field
- Comparison with SegFormer-B5 HOT 1
- ImportError: cannot import name 'revert_sync_batchnorm' from 'mmcv.cnn.utils' HOT 1
- Training gpu consumption vs inference gpu consumption
- about AttentionModule
- If I want to train with 4 GPUs, do I need to change the code
- Evaluation Result on Cityscapes
- How to stop shuffle for test.py
- Config file for the i-SAID dataset
- Can you provide the IOU per class of IsAID dataset?
- 大佬们你们下载的哪个版本的mmcv 为什么我版本较老(mmcv-full1.6.0) 会报错 版本太新(mmcv r4)没有mmcv.runner? HOT 2
- mIoU and MPA
- 随机性问题
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