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ultra-thin-prm's Introduction

The reconstruction implementation of PRM by removing third-party dependency(i.e, Nest).

Motivation: An ultra-thin version of PRM, which aims at improving readability and expansibility.

Rule No.1: Never make code too complicated. 😂

Version info: pytorch 0.4.1, python 3.6

Training & Inference

Training:

python main.py --train True

Inference:

python main.py 

Sample result

Reference

@INPROCEEDINGS{Zhou2018PRM,
    author = {Zhou, Yanzhao and Zhu, Yi and Ye, Qixiang and Qiu, Qiang and Jiao, Jianbin},
    title = {Weakly Supervised Instance Segmentation using Class Peak Response},
    booktitle = {CVPR},
    year = {2018}
}

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ultra-thin-prm's Issues

How to generate the json from the MCG proposals

Hi, thanks for your simplified implementation,
If I want to use this code on COCO, for the input data, how do you obtain the json segmentation files from the coarse MCG proposals, which further processing steps do you perform to obtain the binary map, and then transform it using rle_encode function?

I am looking forward to your reply,

Thanks,

.

最终还是放弃尝试了

How to fine-tune?

Hi, thanks for this repository.
I want to fine-tune the model for a different number of object classes. How can I fine-tune only the head layers with my own small dataset?
Another question, how did you create the json files in the data folder?

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