Comments (4)
应该是cudnn和cuda版本的原因,你可以试一下我构建的docker image:
docker pull zldrobit/onnx:10.0-cudnn7-devel
我用这个image时可以正常转换的
from onnx_tflite_yolov3.
我google了一下,很多人也遇到过这个问题,你可以参考一下:
tensorflow/tensorflow#24828
tensorflow/tensorflow#28326
https://stackoverflow.com/questions/53698035/failed-to-get-convolution-algorithm-this-is-probably-because-cudnn-failed-to-in
或者尝试一下新的配置环境,requirements.txt配置文件已经升级了。
from onnx_tflite_yolov3.
感谢,我在试试。我更改了cudnn版本后,尝试更改pytorch版本和tensorflow版本,最后在tensorflow==1.15.0,并把pytorch==1.3.1的版本换成了CPU版本情况下,运行成功,不报错误。我在训练自己数据集的时,在除数据集参数外,默认配置下,使用这个版本的yolov3检测效果没有yunyang1994的yolov3检测效果好,出现少框和多框的情况,请问您有遇见过吗?
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这个仓库的代码主要是用来验证Darknet weights > ONNX(PyTorch) > TensorFlow > TFLite的转换,暂时没有关注训练效果,如果你想使用PyTorch训练的话可以参考
https://github.com/ultralytics/yolov3
我推荐你先使用原版的yolov3 github仓库训练权重,将得到的weights再用这个仓库进行转换。
这样准确度应该没有问题。
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Related Issues (12)
- Can you provide some details about these lines in yolov5_tflite HOT 2
- The detailed commands for building TFLite GPU delegate so files HOT 3
- An Issue when loading OpenGL backend TFLite GPU delegate in Python HOT 3
- Facing problem while converting Darknet weights to ONNX model for YOLOv3 HOT 5
- 镜像环境运行报错 HOT 3
- Segmentation fault(core dump) HOT 2
- Converting unsupported operation: PyFunc HOT 1
- Use of Reshape feature HOT 4
- Error while trying to convert custom built yolov3 model to tflite model HOT 17
- Segmentation fault (core dumped) in detect.py HOT 2
- Doesn't work with anything remotely recent. HOT 2
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