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Training param tuning
There has two types data augmentation method for different application
https://github.com/eric612/MobileNet-YOLO/issues/29
For example , I will choose adaptive aspect ratio in fisheye videos which were pixel level geometry distortion
- Set preprocessing resize mode to "FIT_LARGE_SIZE_AND_PAD"
- Inference use "FIT_LARGE_SIZE_AND_PAD" resize
This type may break k-mean anchors rule and effect accuracy about 1% in my test
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Set preprocessing resize mode to "WARP"
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Expand param set to {VOC:4.0 , COCO:1.5 , ...}
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Inference use "WARP" resize
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For advance , modify jitter code
unmark
caffe_rng_uniform(1, 1.0f - jitter, 1.0f, &img_h)
and mark
img_h = img_w;
If solver type set to "SGD" , you may need set learning rate policy like this
total_batch_size = iter_size * batch_size
If pre-trained weights use
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Classification model (like imagenet)
total_batch_size set to 64 at least
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Detection model (like ms-coco)
total_batch_size set to 16 at least ("PASCAL-VOC")
total_batch_size set to 32 at least , recommend to 64 ("MS-COCO")
You can try below training technology
- Decrease/Increase expand_param and jitter scale
- Set larger batch size
- Set lr_mult:0.1 or 0.2 and also decay_mult at first 0 ~ x layers (x is 10 for MobileNet) , base on your backbone network
- Changes solver type