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Alternative cloud for trained models #63

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bobetocalo opened this issue Nov 25, 2021 · 7 comments
Closed

Alternative cloud for trained models #63

bobetocalo opened this issue Nov 25, 2021 · 7 comments

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@bobetocalo
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Hello @yangxue0827,

I cannot create a Baidu account. Could you upload the trained models in another cloud such as Dropbox, Onedrive o Google Drive?
If it is not possible ... could you send me all these trained models via email? Thank you so much.

Best regards,
Roberto Valle

@yangxue0827
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I don't have enough space in Google Drive. Which weight do you need, I can send you email.
Please give me the specific configuration file so that I know which weight to download.
example:
cfgs_res50_dota_v4.py
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@bobetocalo
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Thanks @yangxue0827,

I need the three DOTA config files. The most important baselines for me are the following:

RetinaNet-R

cfgs_res50_dota_v7.py
cfgs_res50_dota1.5_v7.py
cfgs_res50_dota2.0_v7.py

FCOS

cfgs_res50_dota_fcos_v1.py
cfgs_res50_dota1.5_fcos_v1.py
cfgs_res50_dota2.0_fcos_v1.py

My email is: bobetocalo@gmail.com

@bobetocalo
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Thank you for the models @yangxue0827

I have some additional questions related to the configuration files.

from configs.DOTA1.5.retinanet import cfgs_res50_dota1.5_v7

@yangxue0827
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sorry, the relevant code of fcos has been uploaded

import the config files:
Copy $PATH_ROOT/libs/configs/#DATASET/#DETECTOR/cfgs_xxx.py to $PATH_ROOT/libs/configs/cfgs.py

@bobetocalo
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Sorry, the relevant code of fcos has been uploaded

I do not understand. How can I reproduce the results of FCOS using your code? Where are the config files for each DOTA protocol?

FCOS 67.69 61.05 48.10

@bobetocalo
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bobetocalo commented Dec 21, 2021

Sorry, the relevant code of fcos has been uploaded

I do not understand. How can I reproduce the results of FCOS using your code? Where are the config files for each DOTA protocol?

FCOS 67.69 61.05 48.10

Hi @yangxue0827, can I use the FCOS algorithm with the current algorithm? I cannot find the FCOS config files.
On the other hand, I suppose that the aforementioned results in https://github.com/yangxue0827/RotationDetection#latest-performance are obtained using train+val images to train the model and private test annotations to measure the performance... Do you have the results of RetinaNet-R and FCOS using validation images to measure the performance?

Thank you.

@yangxue0827
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