Category Archives: machine learning

The uneasy way to implement SSDLite by myself

SSDLite is a variant of Single Shot Multi-box Detection. It uses MobileNetV2 instead of VGG as backbone. Thus it can make detection extremely fast. I was trying to implement SSDLite from the code base of ssd.pytorch. Although it’s not a easy work, I finally learn a lot from the entire… Read more »

Using Single Shot Detection to detect birds (Episode three)

In the previous article, I reached mAP 0.740 for VOC2007 test. After one month, I found out that the key to boost the performance of object detction is not only based on cutting edge model, but also depends on sophisticated augmentation methodology. Therefore I manually checked every image generated by… Read more »

Using Single Shot Detection to detect birds (Episode two)

In the previous article, I reached mAP 0.739 for VOC2007. After about two weeks, I add more tricks to reach mAP 0.740. The most important trick is escalating the expand-scale of augmentation which is made from this patch. Increase the scale range could help the model to detect a smaller… Read more »

Debugging the problem of ‘nan’ value in training

Previously, I was using CUB-200 dataset to train my object detection model. But after I used CUB-200-2011 dataset instead, the training loss became ‘nan’.

I tried to reduce the learning rate, change optimizer from SGD to Adam, and use different types of initializer for parameters. None of these solved… Read more »

Using Single Shot Detection to detect birds (Episode one)

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… Read more »

Some tips about PyTorch and Python

1. ‘()’ may mean tuple or nothing.

The result is:

2. Unlike TensorFlow’s static graph, PyTorch could run neural network just as the code. This means a lot of conveniences. The first advantage, we could print out any tensor in our program, no matter in prediction or training…. Read more »

Summaries for Kaggle’s competition ‘Histopathologic Cancer Detection’

Firstly, I want to thank for Alex Donchuk‘s advice in discussion of competition ‘Histopathologic Cancer Detection‘. His advice really helped me a lot. 1. Alex used the ‘SEE-ResNeXt50’. Instead, I used the standard ‘ResNeXt50’. Maybe this is the reason why my score ‘0.9716’ in public leaderboard is not as good… Read more »

Using XGBoost to predict large sparse data

For using XGBoost to predict, I wrote code like this:

But it reported error:

Seems csr_matrix in SciPy is not supported by XGBoost. Maybe I need to transfer sparse data to dense:

But it still reported:

The ‘test’ data is too big so it cann’t even… Read more »