kaggle CIFAR10 compitition code, implement by mxnet gluon.
we got 0.9688 by merge some ideas from https://discuss.gluon.ai/t/topic/1545/423
| file | descriptor |
|---|---|
| log | some train log file |
| models | some trianed model params(weight) |
| result | the forward result file on kaggle test set |
| submission | the finnal kaggle submission result |
| CIFAR10_train | main train and exp code |
| plot | the visulization of train acc and valid acc and loss with epoch |
| utils/netlib.py | ResNet18, ResNet164_v2, densenet, Focal Loss implement code by gluon, invoke by CIFAR10_train |
| utils/utils.py | some tool function |
models, reuslt, log can get from link: https://pan.baidu.com/s/1pLjzQWj key: f6p3
the main idea is from mxnet topic,we merge the most ideas.
first, we train ResNet164_v2 in diffrent data argument policy
sencondly, we use 'focal loss' replace 'softmax cross entropy loss'
thirdly, we use densenet replace ResNet164_v2
lastly, we ensemble some net to get higher acc, we found this five models get best score:
| policy | kaggle score |
|---|---|
| res164_v2 + DA1 | 0.9529 |
| res164_v2 + DA2 | 0.9527 |
| res164_v2 + focal loss + DA3 | 0.9540 |
| res164_v2 + focal loss + DA3(only use 90% train_data) | 0.9506 |
| sherlock_densenet | 0.9539 |
DA1~DA3 is means diffrent data argument
| DA | policy |
|---|---|
| DA1 | padding image to 40, and then random crop (32, 32)，same as code in sherlock |
| DA2 | resize image to a bigger size，and then crop to (32, 32)，and set data argument parma of HSI to 0.3,PCA noise to 0.01. |
| DA3 | after DA2, clip the color of image to (0,1)（make the generate image more friendly to human） |
we ensemble the five net, and got 0.9688 score.
| 文件名 | 描述 |
|---|---|
| log | 一些训练的日志，主要是训练的loss和acc |
| models | 一些训练的模型参数(权值) |
| result | 程序forward的最终结果，保留了10个类别的output |
| submission | 最终提交的结果文件 |
| CIFAR10_train | CIFAR10上训练模型和产生结果的代码，主程序。 |
| plot | 绘制一些模型训练过程的精度和loss曲线 |
| utils/netlib.py | ResNet18, ResNet164_v2, densenet, Focal Loss 的gluon的实现,被调用。 |
| utils/utils.py | 一些工具函数 |
models、result、log等内容加起来有点大，等传到网盘上，供各位小伙伴参考下载。 链接: https://pan.baidu.com/s/1pLjzQWj 密码: f6p3
参考论坛几个小伙伴的方法，我总结了一下，大致如下:
使用不同的网络的数据增强的方法，我们做了多个实验，得到了多个网络模型(全部放到了models下面)，然后ensemble，发现下面5个网络的效果最好。
这5个网络的训练策略和单独提交的精度分别是：
| policy | kaggle 精度 |
|---|---|
| res164_v2 + DA1 | 0.9529 |
| res164_v2 + DA2 | 0.9527 |
| res164_v2 + focal loss + DA3 | 0.9540 |
| res164_v2 + focal loss + DA3 | 只使用train_data训练: 0.9506 |
| sherlock_densenet | 0.9539 |
上面的DA是3中不同的数据增强的方法:
| DA | policy |
|---|---|
| DA1 | 就是最常用的那种padding到40,然后crop的方法，就是sherlock代码里使用的加强 |
| DA2 | 是先resize到一定的大小，然后crop的方法，同时设置了HSI的几个参数为0.3,PCA噪声为0.01 |
| DA3 | 时在DA2后，将图片的颜色clip导（0,1）之间（动机时创建更符合人感官的自然图片数据） |
五个网络按照各自的精度加权求和作为最后的结果，就有了0.9688的效果。
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