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View Code? Open in Web Editor NEWunofficial EffcientDet implemented by mmdetection
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benuri datakalp qiuhuan huohuotm bruceyang2012 leo-xxx mjanddy arui1 kwaika shiyuan0806 chestnutleeeee altheim ruiningtang mykheievskyi tiber2013 hwijune wulingtian cj401-jw yangshiyu89 dingyf0523 zimoqingfeng marmarello ruihan0495 deep-learnefficientdet's Issues
weight feature fusion seems wrong.
I found your weighted Feature Fusion Module is try to new a tensor as weight, That will make this Tensor be a constant and will not in training.
That parameter may be got from global average polling or similar operation.
about dependency file
thank you for your code, and i want to run rightly them
but i can't find two files, Could you tell me how to get them?
where is the Weighted Feature Fusion?
RT.
how to confing in mmdetection?
how to confing necks in mmdetection?
关于backbone下采样问题
efficientnet中B0 backbone如下所示:
其中stage 2, stage 5并没有下采样，而在efficientdet中采用了最后5个stage, 都进行了下采样
请问这是什么原因?
map is zero
I use your config to train coco, and the loss convergences at about 1.6 and map of coco is 0...
I am debuging the issues....
error result
Did you get a good results? I train the coco ,and get map=0.
Is the paper "EfficientDet: Scalable and Efficient Object Detection" office code?
Thanks for you share.
get poor results
Thanks for your code. But after I replaced fpn with your bifpn in libra rcnn, I got lower map. Did you get higher map in faster rcnn?
run error
when I run the example code
>>> from mmdet.models import EfficientNet
>>> import torch
>>> self = EfficientNet(model_name='tf_efficientnet_b2', pretrained=False)
>>> self.eval()
>>> inputs = torch.rand(1,3,768,768)
>>> level_outputs = self(inputs)
it is error for this:
outs.append(feature_map[i])
IndexError: index 2 is out of bounds for dimension 0 with size 1
how to train？
I found it for a long time, but I didn't find out how to train. Is there no train code and I need to write it myself? Or I didn't find the train interface, If so, can you please give a train and valid tutorial? Thank you very much!
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