SSD (Single Shot Detection) is a type of one-stage object detection neural network which uses multi-scale feature maps for detecting. I forked the code from ssd.pytorch, and added some small modifications for my bird-detection task.
I have tried some different types of rectifier function at first, such as ELU and RRelu. But they only reduced the mAP (mean Average Precision). I also tried to change the hyperparameters about augmentation. But it still didn’t work. Only after I enabled the batch normalization by this patch, the mAP has been boosted significantly (from 0.658 to 0.739).
The effect looks like:

bird detection
Image 1.
bird detection
Image 2.

But actually, we don’t need all types of annotated objects. We only need annotated bird images. Hence I change the code to train the model with only bird images in VOC2007 and VOC2012. Unexpectedly, the mAP is extremely low, and the model can’t even detect all 16 bird heads in the above [Image 2].
Why using bird images only will hurt the effect? There are might be two reasons: first, a too small number of bird images (only 1000 in VOC2007 and VOC2012); second, not enough augmentations.
To prove my hypothesis, I found CUB-200, a much larger dataset for bird images (about 6000). After training by this dataset, the effect is unsatisfied also: it can’t detect all three birds in [Image 1]. I need more experiments to find the reason.