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low performance #4
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Hi, how many GPU cards did you use to train? And which depth maps did you use? By default, we use four GPUs, batchsize=8 and iter=40000 for training. If you use smaller GPUs/batch size training, you can consider reducing the learning rate (e.g. 0.005) and increasing the number of iterations (e.g. 100000 for single card) in training. Thanks. |
Your result is so low that it's strange. Can you provide more details such as the config file? |
Hi, I used 2 GPUs, and the simplified version model(one dilated depth map after 2nd block, and depth maps after 3rd, 4th block, nf=2). I modified the batch size to 2*2, and any other are kept the same as your code. Thanks for your kindly reply! |
Hi, dingmyu: |
@gongshichina
To get more stable results, it is recommended to download the ResNet pre-trained model provided by Ruotian Luo in Google Drive and set
The training log should be displayed as:
|
@DiegoJohnson
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I use 2 GPU with 40000 iterations and bacth size is 2*2, I get result:
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Thanks |
Feel free to reopen it if you have any further questions. |
I will try it, Thanks for your sharing |
@dingmyu Hi, in the link https://drive.google.com/drive/folders/0B7fNdx_jAqhtNE10TDZDbFRuU0E, it didn't have res50_faster_rcnn_iter_1190000.pth or faster_rcnn_1_10_14657.pth, and which model should we download. Thank you for your sharing |
@Hesene |
@Hesene |
Thanks a lot, I get it ,Thank you for your answer again |
When I use your simplified version to train, it produced a bad performance
![1108200878](https://user-images.githubusercontent.com/42108203/71723369-63da9500-2e67-11ea-9b7d-8a5488e665db.jpg)
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