problem in the output

[rmmal]

I have trained a model with this command:

python multigpu_train.py --gpu_list=0,1,2 --input_size=512 --batch_size_per_gpu=14 --checkpoint_path=/backup/EAST/
--text_scale=1024 --training_data_path=/DATA/EAST/data/ --geometry=RBOX --learning_rate=0.0001 --num_readers=12

and i've waited till:

Step 007130, model loss 0.0316, total loss 0.0827, 7.33 seconds/step, 5.73 examples/second

first Question should i make him , do more iterations or this is enough ???

second Question:
The output of all the images seems to be 1 size , why this is happening ?
i couldn't see many variations in the output dimensions

examples:

so what's missing to be able to detect blocks of text ?

[argman]

@rmmal , thanks for trying EAST, this is interesting,
I think you can get reasonable result training until 7000+ iterations.
can you provide a sample annotation of your data ?

In #18 , the result seems nice.

[rmmal]

You are welcome @argman , Great effort btw.

As i said before i had a x1,y1 and x2,y2
where i converted them to x1,y1, x2,y1, x2,y2, x1,y2

so for example the annotations of the last image is

550,1667,833,1667,833,1737,550,1737,text
213,1679,493,1679,493,1731,213,1731,text
230,1741,802,1741,802,1777,230,1777,text
357,1330,687,1330,687,1458,357,1458,text
253,1492,790,1492,790,1539,253,1539,text
159,984,864,984,864,1093,159,1093,text

what could be wrong ? suggestions ?

[rmmal]

setting text-scale 1024 , and input size 512 could make such a problem ?

[argman]

@rmmal , I think your problem is different. The definition of text maybe confusing for the network to learn. In the paper, text is defined as a single line. From your annotation, i cannot find what's the standard of a text line.
e.g. In the last image, why RADO TRUE AUTOMATIC DIAMONDS and HIGH-TECH forms a single block but not two lines

[rmmal]

I edited the dataset and started now training again , I will see what will happen and tell you.

Thanks for your help @argman

[ghost]

@rmmal any updates regarding your training & results?