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zylo117 / Yet-Another-EfficientDet-Pytorch

The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
GNU Lesser General Public License v3.0
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Use pretrained weights from official version or train from scratch #457

Open YangHai-1218 opened 4 years ago

YangHai-1218 commented 4 years ago

Hello, thanks for your work! But I was just wondering how did you get the results? Did you use the pretrained weights from https://github.com/google/automl/tree/master/efficientdet and convert them to pytorch version to load the weights and then use the train.py to fintune the weights? Or you just trained the models from scratch?

TangAL0203 commented 4 years ago

question +１

tetsu-kikuchi commented 4 years ago

I'd like to ask the same question.

zylo117 said that the weights he provides are transferred and finetuned, not trained from scratch, (https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch/issues/140#issuecomment-616344730) while he also said, somewhere in Issues, that he has experiences training a model from scratch by this code.

So I'd like to know whether zylo117 obtained mAP results by models he trained from scratch (possibly with ImageNet-pretrained EfficientNet backbone), which are comparable to mAP results in readme.md.

Thanks.

XhqGlorry11 commented 3 years ago

After looking into so many issues and replies, I really doubt that no one(including zylo117 himself) has ever reproduced his result reported on COCO dataset. It seems that zylo117 just transfer weights from tensorflow and finetune it a little bit. And that should be the reason that why he implements conv and pooling operator exactly the same with tensorflow because he needs to load weights from tf.