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What you should know first:

This post is not finished, I will be keep adding more details in the following days. Also, this post is kept as a continuing test report with BERT and Fastai. I felt like I will lose the courage to write a post every time when I finish the project as the whole. Also, there might be a lot of mistakes as I will frequently report back my finds.

Target audience: People who wants to integrate BERT into fastai for downstream NLP tasks.

There is great post about how this has been down previously, please check out the previous work, most of my understanding is also following the idea in this post. …


Special thanks to fastai leading me into the data science world, after 5 months study, I think it is time for me to post some findings along the path. This post will tend to explain things to beginners, just like me…


If we think computer vision topics like a game, single label classification (dog vs cat) will more like a novice training ground, and multi object detection will be the end game boss.

The path would be, image classification->multi-label classification -> high Imbalanced class classification -> Object detection -> multi-object detection

If you think about real world, you are highly likely encounter imbalanced dataset, such as face detection with one shot learning. One solution would be using Siamese network, then if the item you are interested in is in the part of the image, you want to take it out first. Here is where object detection steps in. …

Hao He

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