Train D0 model on COCO2017 with EfficientNet pre-trained weights && results is not good enough.

[Wanjpeng]

@zylo117 Thanks for your great work! I'm now training EfficientDet-D0 on COCO2017 dataset with EfficientNet pretrained weights(https://github.com/lukemelas/EfficientNet-PyTorch). After around 100 epochs get train loss(total loss:0.221), while val loss(total loss:0.305), and total loss on val set can't descent when reached 0.305. This seemed to be overfitting. Here are my questions:

1. Does the initializing method of BiFPN layers and output layers weights affect the results huge?(For using EfficientNet(backbone) pretrained weights, but not BiFPN and output layers)
2. Whether to change preprocessing if using pretrained weights(EfficientNet weights with advprop).

[zylo117]

1. No.
2. Yes. Without proper preproessing, the pretrained backbone weights is trash

[Wanjpeng]

@zylo117 So, does you mean if I used pretrained weights(with advprop): I should use normalize = transforms.Lambda(lambda img: img * 2.0 - 1.0) rather than:normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])?

[zylo117]

Yes, modify this line.

https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch/blob/master/utils/utils.py#L82

from normalized_imgs = [(img / 255 - mean) / std for img in ori_imgs]

to normalized_imgs = [img / 255 * 2 - 1 for img in ori_imgs]

[Wanjpeng]

@zylo117 Thanks for you help! I will change it and give it a shot.

[Wanjpeng]

@zylo117 Still can not get a good result, the best mAP on COCO2017 valset is only 0.272, which is far lower than yours.

[Wanjpeng]

@zylo117 For overfitting occured, I think train data augmentation should be applied. I was wondering what kind of data augmentation method did you used? In your code there is no any augmentation method used, only image normalizing method. Thanks very much.

[zylo117]

Because the official repo doesn't apply augmentation other than h-flip unless on d6 and d7, neither does mine. Can you share your loss graph? It's overfitting already?

[Wanjpeng]

Here are my training logs.(链接: https://pan.baidu.com/s/1qRd0HiAULa6JfNggc41L5g 提取码: qqc9) Including training logs and tensorboard logs. Thank you very much. @zylo117

[Wanjpeng]

I think it has become overfitting since finetune with efficientnet weights. Around 60 epochs(train_woadv_7.10_09.56.log).

[zylo117]

It has. Maybe try larger batchsize or SGD.

[Wanjpeng]

So, did you meet with the overfitting problem?

[Wanjpeng]

Hello, @zylo117 , I read the official EfficientDet repo and find this, the official codes does use these augmentation methods. (Official repo: aug/autoaugment.py/distort_image_with_autoaugment())

[Wanjpeng]

[bakirsw]

Hi mate @Wanjpeng Can you please share your changes in the code? I want to train the D0 model on coco2017 with EfficientNet pretrained weights