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onnx
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【已解决】关于AP=0的问题
我发现很多人在使用voc格式的数据集时,和我遇到了同样的问题,训练时AP一直为0,
今早,仔细检查后,我也找到了真正的原因,主要是数据加载的地方出现了问题,还是我们自己太不仔细了
解决流程思路: 解决YOLOX训练时AP为0
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请问可以直接training tmfile出来吗? 因为tengine-convert-tool covert 会有error
tengine-lite library version: 1.4-dev
Get input tensor failed
或是有例子能training出下面tmfile 呢?
 where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?
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I am trying to convert a custom pytorch model to tensorflow, I am abe to convert pytorch to onnx but converting onnx to tensorflow gives issue.
The code snippets are as follows-
pytorch to onnx
net = custom pytorch model
net.load_state_dict("pre-trained model")
dummyInput = np.random.uniform(0,1,(1,8,3,256,256))
dummyInput = Variable(torch.FloatTensor(dummyInput))
torch.onnx.export(ne
'max_request_size' seems to refer to bytes, not mb.
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New Operator
Describe the operator
Why is this operator necessary? What does it accomplish?
This is a frequently used operator in
tensorflow/keras
Can this operator be constructed using existing onnx operators?
If so, why not add it as a function?
I don't know.
Is this operator used by any model currently? Which one?
Are you willing to contribute it?