识别红绿灯比赛：
博客：
https://medium.freecodecamp.org/recognizing-traffic-lights-with-deep-learning-23dae23287cc
Software and hardware
I used Caffe to train the models. The main reason I chose Caffe was because of the large variety of pre-trained models.
Python, NumPy & Jupyter Notebook were used for analyzing results, data exploration and ad-hoc scripts.
Amazon’s GPU instances (g2.2xlarge) were used to train the models. My AWS bill ended up being $263 (!). Not cheap. ????
The code and files I used to train and run the model are on GitHub.
github地址：
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.
Since the competition rewards solutions that use small models, early on I decided to look for a compact network with as few parameters as possible that can still produce good results. Most of the recently published networks are very deep and have a lot of parameters. SqueezeNet seemed to be a very good fit, and it also had a pre-trained model trained on ImageNet available in Caffe’s Model Zoo which came in handy.
鲸鱼识别比赛：
https://deepsense.ai/deep-learning-right-whale-recognition-kaggle/
SqueezeNet变种：