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Only got 76.90% over Pascal VOC2012 val set #41
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I simply ported to python3 and tried with mxnet 1.0, then get the results as the author's.
maybe you would try the newest version, mxnet 1.0 |
I too am now trying to reproduce results. It would be really helpful if we could get version specifications for packages used to achieve the published results (e.g. a pip requirements.txt would be great). I am achieving 75-76% using:
I have begun trying to port the code to Python 3, but I am hoping to retrain this network on my own data, so I fear that might be complicated, but I'll plan to post an update later. |
What's your training parameters?
mcever <notifications@github.com> 於 2019年3月6日 週三 上午8:19寫道:
… I too am now trying to reproduce results. It would be really helpful if we
could get version specifications for packages used to achieve the published
results (e.g. a pip requirements.txt would be great). I am achieving 75-76%
using:
(ademxapp) ***@***.***:~/ssd/ademxapp$ pip freeze DEPRECATION: Python
2.7 will reach the end of its life on January 1st, 2020. Please upgrade
your Python as Python 2.7 won't be maintained after that date. A future
version of pip will drop support for Python 2.7. certifi==2018.11.29
chardet==3.0.4 graphviz==0.8.1 h5py==2.9.0 idna==2.6 mxnet-cu90==1.4.0
numpy==1.13.3 Pillow==5.4.1 PyYAML==3.13 requests==2.21.0 six==1.12.0
urllib3==1.22
(ademxapp) ***@***.***:~/ssd/ademxapp$ python issegm/voc.py --data-root
data/VOCdevkit --output output --phase val --weights
models/voc_rna-a1_cls21_s8_coco_ep-0001.params --split val --test-scales
500 --test-flipping --gpus 0 . . . 2019-03-05 16:06:11,561 Host pixel acc:
93.92%, mean acc: 87.64%, mean iou: 75.17% 2019-03-05 16:06:11,562 Host
[95.27 96.00 87.58 93.95 82.92 81.21 97.88 92.68 97.04 59.27 95.60 69.96
92.80 95.10 93.32 94.92 73.03 95.16 72.35 92.74 81.57] 2019-03-05
16:06:11,562 Host [92.70 83.09 44.98 83.29 67.80 70.50 92.56 85.14 86.84
43.02 84.93 61.79 83.36 83.14 78.65 82.37 56.09 83.02 55.05 85.35 74.76]
2019-03-05 16:06:11,562 Host Done in 815.40 s.
I have begun trying to port the code to Python 3, but I am hoping to
retrain this network on my own data, so I fear that might be complicated,
but I'll plan to post an update later.
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I have not begun training yet. I was hoping I could simply reproduce the reported results with voc_rna-a1_cls21_s8_coco_ep-0001.params before trying to train on my own data. You can see exactly the command and its output above. |
@mcever hello,I follow your setting for python2.7, and I test the model with voc_rna-a1_cls21_s8_coco_ep-0001.params provided by the author. I only got the miou result is 45.38%. Do you know the reason or can you give me some advice? Thanks~ |
Hi,
I tried the Pascal VOC2012 trained model provided in the repository. However, I only got 76.90%, instead of 80.84% reported in the README.. I used the latest MXNet (v0.11.0). Do you have any idea?
Here is my log for the last three images.
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