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fresh man ask for help about the dataset #4

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OliverHxh opened this issue Jul 17, 2019 · 16 comments
Open

fresh man ask for help about the dataset #4

OliverHxh opened this issue Jul 17, 2019 · 16 comments

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@OliverHxh
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OliverHxh commented Jul 17, 2019

hi~~~
i am a new man in this. your repository is amazing for me, but i have a problem with the dataset.maybe its not a big problem for u.
i already download the Pelee_VOC.pth and put it to weights folder, and i follow the steps to download the dataset VOC2007, and it looks like this.
image
and i run the command
python demo.py --dataset VOC\VOC2007 --config ./configs/Pelee_VOC.py --trained_model ./weights/Pelee_VOC.pth --show
but it comes out the
image
i dont whats wrong? could u help me? thx~~~

@OliverHxh
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em....
worong folder path
i was so stupid...

@Z-Z-Han
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Z-Z-Han commented Jul 27, 2019

@Jaychoukobbe Hi! I also meet a similar KeyError. Could you tell me what the right way to set folder path is? Currently I put VOC data at home: ~/data/VOCdevkit and ~/coco.

@OliverHxh
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@Z-Z-Han if u wanto train on VOC, just use VOC is ok , because in Pelee_VOC.py file,
image
this shows the dataset path, that all u need to do is to tell it what dataset u wanna train on
VOC
or
COCO

@Z-Z-Han
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Z-Z-Han commented Jul 27, 2019

@Jaychoukobbe Get it! Thank you very very much!

@OliverHxh
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@Z-Z-Han i am still working on this codebase, if u have any question, u are welcome to ask...

@zhuixunforever
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@Jaychoukobbe @Z-Z-Han @yxlijun did you train your own dataset? or train coco/voc dataset by your self? did you success with this code? and how long had you been training??

@OliverHxh
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@zhuixunforever i replace backbone with shuffleNetV2 and i train on VOC, i use 1080i gpu and almost train 3days, 400 epoch. by the way my batchsize is 64. the final MAP result is 0.62 when i use shufflenetV2 1X, and 0.67 when i use shufflenetV2 2X

@Z-Z-Han
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Z-Z-Han commented Aug 7, 2019

@zhuixunforever I trained the model on my dataset with over one hundred images,using 1080 gpu. The time for training is about on day, and I only change batchsize to 32. The performance is a little worse than ssd with vgg as backbone, but also acceptable. For an image with size 640*480, the inferencing time is about 1~2 seconds when using cpu.

@OliverHxh
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@Z-Z-Han what dataset u train on? and waht map u get?

@Z-Z-Han
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Z-Z-Han commented Aug 7, 2019 via email

@Z-Z-Han
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Z-Z-Han commented Aug 7, 2019 via email

@OliverHxh
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@Z-Z-Han have ever tried on public dataset , like voc or coco?

@Z-Z-Han
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Z-Z-Han commented Aug 7, 2019 via email

@OliverHxh
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@Z-Z-Han all right

@duducode
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@zhuixunforever I trained the model on my dataset with over one hundred images,using 1080 gpu. The time for training is about on day, and I only change batchsize to 32. The performance is a little worse than ssd with vgg as backbone, but also acceptable. For an image with size 640*480, the inferencing time is about 1~2 seconds when using cpu.

hao can i running it on cup,can you help me

@Rafay2919
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@Z-Z-Han i am still working on this codebase, if u have any question, u are welcome to ask...

i have following error "ImportError: No module named cpu_nms" how did you solve this.

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