Understanding Clouds from Satellite Images比赛的discussion调研与colab数据集下载配置
colab数据集下载配置代码：
%%time
!pip install -U -q kaggle
!mkdir -p ~/.kaggle!echo '{"username":"pupil1","key":"ae776d041bf94ae1bfa9a3843797ad6d"}' > ~/.kaggle/kaggle.json!chmod 600 ~/.kaggle/kaggle.json
!mkdir -p understanding_cloud_organization
!kaggle competitions download -c understanding_cloud_organization
!mv *.zip understanding_cloud_organization/
!mv *.csv understanding_cloud_organization/
!cd /content/understanding_cloud_organization/;unzip train_images.zip
!cd /content/understanding_cloud_organization;mkdir train_images;mv *.jpg train_images/
!cd /content/understanding_cloud_organization/;unzip train.csv.zip
!cd /content/understanding_cloud_organization/;unzip test_images.zip
!cd /content/understanding_cloud_organization;mkdir test_images;mv *.jpg test_images根据[2]的描述
The remaining area, which has not been covered by two succeeding orbits, is marked black.0
所以图片中如果出现黑色区域，就是两颗卫星都没有扫描到的地方。如下：
使用pupil1账号视角,凡是变色的都是看过的,实在极其没有意义的不予收录.
| 链接 | 备注 |
| Train with crops, Predict with full images | 发帖子的人得分不高 |
| How effective is pseudo-labeling? | (看完了)半监督 |
|
[LB 0.628] simple segmentation approach threshold is high? |
threshold的用法 |
|
Overlapping Labels in Train Data? Can a pixel be considered as multiple classes? |
(看完了) 根据第一个链接,一个像素可以属于多个类别. Each image was labeled by several people (2-4), so the labels can overlap. In addition, there was no restriction that the labels from a single labeler cannot overlap. To create the masks for this competition, we simply used the union of all labels for each class. So naturally there will be some overlap. |
| AdamAccumulate | (看完了)提到了AdamAccumulate的版本兼容性问题 |
| Hints for late joiners? | (看完了)提到使用steel比赛的方案 |
| Bounding Boxes instead of Segmentation |
(看完了)评论中提到: 举办方不鼓励对象检测的方式,但是帖子的作者认为线性的模型比非线性的模型跟容易泛化,所以坚持使用Bounding Boxes(对象检测)的方式 |
| use linknet | unet> linknet > fpn |
| Correct Dice Metric | (看完了)讨论误差函数机制 |
| Instance Segmentation->Request for list of past competition | 参考资料 |
|
Information: Bad image list Corrupt and Mislabeled Images Information: Bad image list |
一些损坏的数据 |
| Question about the black area in the image | 有很好的可视化 |
| ResNet34 implementation of Unet works but ResNet 50 and 101 fails? | (看完了)改变模型如果爆内存就减少batch_size |
| Flowers are easy to pick ? | 介绍了一些树算法 |
| Single model performance | 最佳单模 |
| A best description of Generating mask from encoded pixel | 涉及encoded pixel |
|
Adding TTA to the model before optimisation could help Augmentations Strategies for this Competition. TTA? |
使用时间强化 |
|
Augmentations thred Augmentations released version 0.4.0 |
图像增强的讨论 |
| Questions about the origin of the data | 讨论快照功能 |
|
More Tricks to Train w/ Bigger Batches (pytorch) Some tricks to train faster (pytorch) A trick to use bigger batches for training: gradient accumulation |
讨论训练技巧 |
| Simple Descriptions of Cloud Types / Labeling Process | 讨论肉眼区分类别 |
| Fast data loading [Experiments] | 快速读取数据 |
| Deeper, Stronger, Better? |
发现resnet18有效
|
| Beware of Pandas value_counts method for validation split | 指出几个代码的pandas使用有误 |
| Efficient Net B4-B7 | 评论区提到修补小batch_size的办法是使用 gradient accumulation |
|
Improving code quality with utility scripts Utility scripts for Keras users Using High-level frameworks is not learner friendly |
代码推销 |
| Object Detection vs Instance Segmentation | 很多概念 |
| Hybrid convolutional and bidirectional LSTM or RNN | 使用RNN网络 |
| EfficientNets are now available in pytorch segmentation model repo. | 没看懂这个是干嘛的,房之后再看 |
| New method to tackle severe label noise | 处理label噪音的一篇论文 |
| FPN or Unet: Which one is better? | 提到了FPN以及Unet |
| Some thoughts on this competition | kernel grandmaster的一些想法 |
| what is the label to be taken for overlapping masks? for example, in the image 0011165.jpg, Fish and Flower masks overlap each other for some region. | mask重合 |
| Must read material | 一些资料 |
|
Ideas for merging ensemble's predictions How to effectively ensemble models with Keras |
讨论模型融合 |
| Instance Segmentation->How to predict classes | 讨论UNET的输出怎么改成多分类 |
| What does it mean to use a pretrained resnet encoder with UNET? | 讨论UNET使用预训练的resnet编码器是什么意思? |
| Regular image segmentation approach | 提到进行语义分割任务的都有两个数据集 |
| Discussing post processing | 讨论后处理 |
| Weakly supervised segmentation | 弱监督分割 |
| Must-see Kernels and topics - Understanding Clouds from Satellite Images | 对于资料的自行总结 |
| RLE Decode in C++ | 提到了RLE技术 |
| Hints from a late joiner's persepctive | 提到了后处理 |
| Impact of using classier for removing the masks | 考虑去掉mask编码 |
| A Late Joiner's Understanding and Notes | 需要细看 |
| LPT: See what's going on with that commit ? | 介绍了一个有用的训练的可视化工具 |
| Knock Knock can send you email notification (or slack notification) | 一个工具用来提醒你训练结束的时候发信息到你邮件通知你 |
一些统计数据来自[1]:
Useful Stats::
no. of empty mask = 7055
no. of non-empty mask = 7737
no. of non-empty mask for Fish = 1864
no. of non-empty mask for Flower = 1509
no. of non-empty mask for Gravel = 1982
no. of non-empty mask for Sugar = 2382
Reference:
[1]Public TestSet Distribution via LB probing
[2]https://www.kaggle.com/c/understanding_cloud_organization/data
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