Comments (5)
shicai
commented on May 13, 2024
here is an example, batch size is 256.
net: "train_val.prototxt"
#test_initialization: false
#test_iter: 100
#test_interval: 5000
display: 20
average_loss: 20
base_lr: 0.1
lr_policy: "poly"
power: 1.0
max_iter: 500000
momentum: 0.9
weight_decay: 0.0001
snapshot: 5000
snapshot_prefix: "mobilenet"
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CFAndy
commented on May 13, 2024
Great! Thanks!
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CEDAclaire
commented on May 13, 2024
When should those parameters be uncommented?
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shicai
commented on May 13, 2024
@CEDAclaire when you have enough gpu memory or you don't meet any memory problems/errors...
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cindycfeng
commented on May 13, 2024
Can you please post the solver for Mobilenet V2?
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Related Issues (20)
- About Top1 and Top5 result HOT 5
- mobilenet_v2 MAdds 429M vs paper's 300M, because of two difference HOT 1
- Is it recommended to use global batch for fine tuning?
- I wonder something intresting why it's
- image size
- Fine-tune with provided solver leads to loss=87.3365 HOT 6
- The accuracy i test is lower than that in the readme file HOT 1
- Check failed: target_blobs.size() == source_layer->blobs().size() (1 vs. 0) Incompatible number of blobs for layer conv5_5/dw HOT 1
- Some bottlenecks do not contain skip connections in Mobilenet v2?
- MobileNet025
- can you provide the prototxt generator ? thank you very much
- bug in eval_image,py
- caffe可以实现宽度乘法器和分辨率乘法器吗？
- check failure stack trace
- Training imagenet from scratch
- can you share the training code?
- batchnorm layer variance exist negative numbers HOT 3
- TypeError: slice indices must be integers or None or have an __index__ method HOT 4
- Mobilenet-NNIE HOT 2
- Wrong predictions after fine-tuning the mobilenetv2 model HOT 4
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