Comments (15)
Here is a link 链接: https://pan.baidu.com/s/1vZONIe2pBkxjo-s5wP3zAg 密码: 3ip6.
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step 1: unzip the file in $caffeSSD/models directory
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step 2: modify batch size in pelee_voc/train.prototxt, I modify batch size 32 to 20 because I only have 8 GB memory, you can revert batch size to 32
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step 3:
cd $caffeSSD
./build/tools/caffe train -solver=models/pelee_voc/solver.prototxt
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@MrWhiteHomeman I am trying to do it, response to you later
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@ujsyehao It is so nice of you for your reply !!! I have another question about the code, in the feature_extractor.py ,the 30th line, why are there two 'stage4_tb/ext/pm2/res/relu' in the Pelee.mbox_source_layers? Can you give me some advices? Thank you!!!
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You can use (http://ethereon.github.io/netscope/#/editor) to view peleenet-ssd network structure, you will find stage4_tb/ext/pm2/res layer is used twice to generate ext/pm1_mbox_loc layer and ext/pm2_mbox_loc layer(conf layer/priorbox layer is also the case).
The reason is that peleenet drops 38x38 feature map(you can view pelee paper) and just use the remaining 5 feature extracted layer(19x19, 10x10, 5x5, 3x3, 1x1), but SSD merges 6 layers' prior boxes, so author use 19x19 feature map(also known as stage4_tb/ext/pm2/res) twice to predict two conf/loc/priorbox layers.
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mobile-net ssd also follows this design pattern, I will update later.
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@ujsyehao Hello,I have a question about the batchsize, in this paper , the batch-size is 32, if I change the batch-size to 64, will I get a better result about testing ? I would appreciate it if you could give me some advices.
Thank you!!!
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No, batch size can affect training time and has no direct relation with model performance.
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@ujsyehao So,I always have a question about batchsize, if the batch size is too big, will it have a bad result? And I know the DetNet(旷世科技) , it set the batch size to 256, and get a greatest result...
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You can look it
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Could you please share the prototxt again? The link [链接: https://pan.baidu.com/s/1vZONIe2pBkxjo-s5wP3zAg 密码: 3ip6.] failed now.
Thank you very much~~~ @MrWhiteHomeman @ujsyehao
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@ujsyehao
所谓的batch size大小不会影响model performance,应该仅限于模型中的BN层参数固定的情况吧.
如果BN层的参数在训练过程中也进行微调,那么batch size大小还是会影响model performance的吧.
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@foralliance model performance 依赖于模型本身,batch size只是一个超参而已,如果你修改了batch size,再选定其它合适的超参比如base_lr,它最终的效果是一样的,一般而言,使用一个大batch只是训练的更快,更容易出结果,并不会从根本上决定模型的性能
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@ujsyehao HI
这是之前和一位作者关于batch size的讨论
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@foralliance 我看了这个回答,默认accum_batch_size固定,对于无BN层/BN层参数固定情况下,batch size不影响模型性能这个观点我是认同的
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各位大佬好,你们谁晓得Pelee训练目标检测 + 车道线,谢谢各位
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Related Issues (20)
- fine tuning with different number of classes HOT 1
- peele-SSD add_extra_layers_pelee
- About the stanford dog dataset. HOT 3
- 2-way dense layer in code and paper seems mismatch. HOT 2
- how can i get the fps=120 on nvidia tx2? please help me HOT 1
- Calculation of number of parameter, macc, and flops HOT 3
- pytorch pretained model
- max_iter
- Does it support 512 or bigger input size? HOT 1
- question for iteration HOT 1
- can not download the pretrained PeleeNet model
- one question
- Question for peleeNet structure
- How can I train my own model?
- train error
- peleeNet speed in GTX1080ti HOT 1
- Question about 1x1 convolutional kernels to reduce computational cost
- the paper was accepted two years ago???
- Pelee input resolution problem
- question about merge bn?
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