Category Archives: machine learning

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 »

Some summaries for Kaggle’s competition ‘Humpback Whale Identification’

This time, I only spent one month on competition “Humpback Whale Identification”. But still, get a little step forward than previous competitions. Here are my summaries: 1. Do review ‘kernels’ in competition page, this will teach me a lot of information and new technology. By using Siamese Network rather than… Read more »

Using ResNeXt in Keras 2.2.4

      3 Comments on Using ResNeXt in Keras 2.2.4

To use ResNeXt50, I wrote my code as the API documentation for Keras:

But it reported errors:

That’s weird. The code doesn’t work as documentation said. So I checked the code of Keras-2.2.4 (the version in my computer), and noticed that this version of code use ‘keras_applications’ instead… Read more »

Some tips about using Keras

      No Comments on Some tips about using Keras

1. How to use part of a model

The ‘img_embed’ model is part of ‘branch_model’. We should realise that ‘Model()’ is a heavy cpu-cost function so it need to be create only once and then could be used many times. 2. How to save a model when using ‘multi_gpu_model’… Read more »