About pretrained models to generate 36 per image feature

[masonwang96]

Thank you for your excellent work!
I have a question. I'm using this bottom up attention method to generate 36/image feature. In your README, you wrote that:

To recreate the pretrained feature files with 36 features per image, set MIN_BOXES=36 and MAX_BOXES=36 use this alternative pretrained model instead. The alternative pretrained model was trained for fewer iterations but performance is similar.

which is resnet101_faster_rcnn_final_iter_320000.caffemodel.
However, when I was doing caption task, I generateed 36/image features with resnet101_faster_rcnn_final.caffemodel , and found that features generated this way is better than waht you recommended above.

So I was wondering why? Does resnet101_faster_rcnn_final.caffemodel is better anyway? Looking forward to your reply. Thanks a lot.
@peteanderson80

[masonwang96]

In other words, why did you recommend using resnet101_faster_rcnn_final_iter_320000.caffemodelto generate 36/image features?

[nyj-ocean]

@masonwang96
where to download the resnet101_faster_rcnn_final.caffemodel？
can you share the url？

[masonwang96]

https://www.dropbox.com/s/5xethd2nxa8qrnq/resnet101_faster_rcnn_final.caffemodel?dl=1

[alice-cool]

#87 (comment)

Dear scholar,
I can't download the model resnet101_faster_rcnn_final_iter_320000.caffemodel.
I think maybe this model can generate the similar features provided by the author.
So the author maybe doesn't mean the resnet101_faster_rcnn_final_iter_320000.caffemodel model will get better performance than the resnet101_faster_rcnn_final.caffemodel(10~100).
If you have BaiduWangpan, could you share the model file "resnet101_faster_rcnn_final_iter_320000.caffemodel" with me ?
I am so eager to attain the model file.

[masonwang96]

Sorry about the delay. I have shared the "resnet101_faster_rcnn_final_iter_320000.caffemodel" through BaiduWangpan. The link is as follows:

链接: https://pan.baidu.com/s/18TGdxmx_IxCqbzx2APEd2w 密码: 8aa4

[masonwang96]

By the way, this is the "resnet101_faster_rcnn_final.caffemodel":

链接: https://pan.baidu.com/s/1ELt5hL1BKemfDYDyN1dOPw 密码: altw

[alice-cool]

Thanks DaLao，Pour a bottle of coca cola for you. 

…

---Original--- From: "Mason Wang"<notifications@github.com&gt; Date: Sat, Feb 27, 2021 14:25 PM To: "peteanderson80/bottom-up-attention"<bottom-up-attention@noreply.github.com&gt;; Cc: "Comment"<comment@noreply.github.com&gt;;"Donglearner"<2524981200@qq.com&gt;; Subject: Re: [peteanderson80/bottom-up-attention] About pretrained models to generate 36 per image feature (#87) Sorry about the delay. I have shared the "resnet101_faster_rcnn_final_iter_320000.caffemodel" through BaiduWangpan. The link is as follows: 链接: https://pan.baidu.com/s/18TGdxmx_IxCqbzx2APEd2w 密码: 8aa4 — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe.

[alice-cool]

Thanks for your timely share! Thanks again.
And I run the generate_tsv.py, with the resnet101_faster_rcnn_final_iter_320000.caffemodel that you share, and the result can be reproduced.
with the same picture, COCO_train2014_000000150367.jpg. The boxes is the same.

The result run by generate_tsv.py
[[ 0. 0. 608.7625 350.63208 ]
[276.54065 54.26455 639.2 454.42773 ]
[ 0. 18.055298 286.9795 464.57227 ]
[ 0. 277.6827 583.3728 479.2 ]
[ 26.716516 131.85942 322.95587 259.661 ]
[386.7497 44.484303 522.03094 154.81404 ]
[ 75.55986 327.47873 421.8147 463.39697 ]
[306.05084 353.73135 386.62222 454.88385 ]
[242.39853 355.86627 333.31012 453.9502 ]
[263.6872 144.34995 479.49268 319.98187 ]
[279.20203 0. 584.5702 132.85336 ]
[584.67584 0. 639.2 107.73751 ]
[300.33197 265.5234 387.11652 347.883 ]
[ 0. 0. 252.79614 140.41837 ]
[452.19727 384.70477 542.59 425.482 ]
[141.57083 140.46927 455.4457 344.03836 ]
[118.00602 138.60199 289.6468 235.06424 ]
[321.5712 358.26862 377.40723 448.2745 ]
[277.84735 345.26016 382.9519 455.149 ]
[338.34506 0. 639.2 164.73306 ]
[ 39.608967 170.50273 219.11008 257.4533 ]
[503.02118 49.59502 582.64844 156.79427 ]
[280.75128 146.12346 347.84033 265.48737 ]
[156.60852 352.786 344.48508 450.08588 ]
[ 0. 302.57465 78.91972 395.2979 ]
[ 27.668285 4.043219 181.89998 120.18465 ]
[ 0.86831665 315.65247 70.51543 366.01852 ]
[403.1822 65.44243 437.54654 103.289085 ]
[263.4151 366.8589 314.9948 444.99268 ]
[440.21368 156.93144 500.5992 193.9488 ]
[169.18588 359.8093 271.23724 456.87762 ]
[203.40878 2.5947204 379.92694 80.30624 ]
[389.56876 239.18001 475.52667 330.16315 ]
[149.9029 152.9564 193.36127 188.3182 ]
[158.44717 0. 214.74826 108.832054 ]
[ 67.062485 213.83627 249.61057 352.60757 ]]
GPU 0: 1/1 0.964s (projected finish: 0.00 hours)

The train2014.zip
array([[ 0. , 277.6827 , 583.3728 , 479.2 ],
[389.56876 , 239.18001 , 475.52667 , 330.16315 ],
[300.33197 , 265.5234 , 387.11652 , 347.883 ],
[118.00602 , 138.60199 , 289.6468 , 235.06424 ],
[ 0. , 0. , 252.79614 , 140.41837 ],
[156.60852 , 352.786 , 344.48508 , 450.08588 ],
[280.75128 , 146.12346 , 347.84033 , 265.48737 ],
[386.7497 , 44.484303 , 522.03094 , 154.81404 ],
[338.34506 , 0. , 639.2 , 164.73306 ],
[321.5712 , 358.26862 , 377.40723 , 448.2745 ],
[279.20203 , 0. , 584.5702 , 132.85336 ],
[263.4151 , 366.8589 , 314.9948 , 444.99268 ],
[440.21368 , 156.93144 , 500.5992 , 193.9488 ],
[ 0. , 0. , 608.7625 , 350.63208 ],
[452.19727 , 384.70477 , 542.59 , 425.482 ],
[ 0.86831665, 315.65247 , 70.51543 , 366.01852 ],
[149.9029 , 152.9564 , 193.36127 , 188.3182 ],
[ 27.668285 , 4.043219 , 181.89998 , 120.18465 ],
[276.54065 , 54.26455 , 639.2 , 454.42773 ],
[141.57083 , 140.46927 , 455.4457 , 344.03836 ],
[584.67584 , 0. , 639.2 , 107.73751 ],
[169.18588 , 359.8093 , 271.23724 , 456.87762 ],
[158.44717 , 0. , 214.74826 , 108.832054 ],
[ 0. , 302.57465 , 78.91972 , 395.2979 ],
[ 0. , 18.055298 , 286.9795 , 464.57227 ],
[503.02118 , 49.59502 , 582.64844 , 156.79427 ],
[ 75.55986 , 327.47873 , 421.8147 , 463.39697 ],
[403.1822 , 65.44243 , 437.54654 , 103.289085 ],
[277.84735 , 345.26016 , 382.9519 , 455.149 ],
[306.05084 , 353.73135 , 386.62222 , 454.88385 ],
[242.39853 , 355.86627 , 333.31012 , 453.9502 ],
[203.40878 , 2.5947204 , 379.92694 , 80.30624 ],
[263.6872 , 144.34995 , 479.49268 , 319.98187 ],
[ 26.716516 , 131.85942 , 322.95587 , 259.661 ],
[ 39.608967 , 170.50273 , 219.11008 , 257.4533 ],
[ 67.062485 , 213.83627 , 249.61057 , 352.60757 ]],
only the order is placed

[masonwang96]

Good news!

[tejan-rgb]

Hi,

I am having a problem while running the code. Please help me.
This is the error I am having:

Error under config key: TRAIN
Traceback (most recent call last):
  File "./tools/generate_tsv.py", line 202, in <module>
    cfg_from_file(args.cfg_file)
  File "/home/tejan/bottom-up-attention/tools/../lib/fast_rcnn/config.py", line 290, in cfg_from_file
    _merge_a_into_b(yaml_cfg, __C)
  File "/home/tejan/bottom-up-attention/tools/../lib/fast_rcnn/config.py", line 277, in _merge_a_into_b
    _merge_a_into_b(a[k], b[k])
  File "/home/tejan/bottom-up-attention/tools/../lib/fast_rcnn/config.py", line 262, in _merge_a_into_b
    raise KeyError('{} is not a valid config key'.format(k))
KeyError: 'HAS_RPN is not a valid config key'

P.S. I m using only CPU. I don't have any GPU. Is there some change that I need to make in the code?

[khk-abc]

hi!, could you give a share relevant prototxt about the the model