關於 kaggle autonomous driving ，我們在網路上蒐集到這些相關的討論、資訊與評價

「kaggle autonomous driving」的推薦目錄：

關於kaggle autonomous driving 在 [心得] kaggle競賽回顧PKU Autonomous Driving - 批踢踢實業坊的評價
關於kaggle autonomous driving 在 DrivingStereo | A Large-Scale Dataset for Stereo Matching in ... 的評價
關於kaggle autonomous driving 在 Waymo Open Dataset - GitHub 的評價
關於kaggle autonomous driving 在就立即加入Facebook 吧。的評價
關於kaggle autonomous driving 在 Kaggle-Peking-University-Baidu-Autonomous-Driving-32 ... 的評價
關於kaggle autonomous driving 在 [心得] kaggle競賽回顧PKU Autonomous Driving - Mo PTT 鄉公所的評價
關於kaggle autonomous driving 在 How our team got a Silver medal in Kaggle competition Lyft ... 的評價
關於kaggle autonomous driving 在 How to download dataset of Kaggle to Colab - Stack Overflow 的評價

社群媒體上有些相關的討論：

kaggle autonomous driving 在 [心得] kaggle競賽回顧PKU Autonomous Driving - 批踢踢實業坊的推薦與評價

作者outrunner (ot)

看板DataScience

標題[心得] kaggle競賽回顧 PKU Autonomous Driving

時間Fri Jan 22 18:35:17 2021

一年前的今天，2020/1/22日本時間上午九點，
我在豐岡往京都的JR特急上等待kaggle競賽開獎。
由於很早就安排帶小孩出遊，所以最後幾天
只用預先準備好的submission玩Fit LB的遊戲。
話說運氣一直都是打kaggle很重要的一部分。

https://www.kaggle.com/c/pku-autonomous-driving/leaderboard

這場比賽是預測圖像中車輛的6個自由度(6 degrees of freedom)
也就是相對於攝影機的平移(x, y, z)與旋轉(yaw, pitch, roll)，
評估指標是mAP

輸入圖像：

標記是每輛車的6個自由度，太小的車輛提供mask不予計算。
根據ground truth可以畫出如下示意圖：

每場比賽都有許多大大小小的細節，但過了一年
我想這場只有一個關鍵值得一提。

基本解法是拿物件偵測模型改輸出就不用多說了。

由於訓練資料只有四千多張，所以在看了一些方法後，
就把競賽重心轉移到資料增強方面。
因為這場比賽的資料特性，使得一般常用的旋轉平移裁切都無法使用。

# 看來只能從旋轉攝影機下手

# 意外的是在這個領域沒看過使用這個方法

kaggle好玩就在這裡，有時候你只要比別人多一招就足以影響比賽結果。
更何況這個方法不但增加source的變化，同時target也改變了，堪稱完美。

舉例說明實際的操作結果。
底下有兩張圖，一張原始資料，一張是攝影機原地向左旋轉50度：

原圖最左邊那台車相對應的6個自由度：

x y z yaw pitch roll
原始 -3.28 2.79 8.51 0.159 -0.009 -3.091
左轉50度 4.42 2.79 7.98 0.064 -0.883 -2.987

左右轉50度的動畫圖：

三個維度都可以自由旋轉，詳情請看範例：

https://www.kaggle.com/outrunner/rotation-augmentation

說穿了不就只是個資料增強？
差別在於同一台車同樣背景，會因為出現在
畫面中的位置、角度與透視變形不同，而有不同的答案。
剛好我們希望模型學到的是"物件的位置、角度與透視變形"，
而不是"物件本身與背景"。

豐富的資料也讓模型更強健，
這可能也是最後在private test set勝出的原因。
如果你的模型沒看過天上飛的車子，
就無法對飛天車做出正確的預測。(這就是deep learning)

當然啦，如果要再吹毛求疵一點，
這個操作是假設攝影機是所謂的"理想攝影機"。
不過當你面對僅有四千張圖以及標記誤差的時候不用想這麼多。

# 結果就是mAP大約多10%

解法全部內容：

https://www.kaggle.com/c/pku-autonomous-driving/discussion/127037

競賽總結：

# 一個資料增強的方法使分數多10%，而且只有我在用

~ 你怎麼能說kaggle不好玩

歡迎討論 :)

--

※ 發信站: 批踢踢實業坊(ptt.cc), 來自: 36.230.238.138 (臺灣)
※ 文章網址: https://www.ptt.cc/bbs/DataScience/M.1611311719.A.B7E.html

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一年前的今天，2020/1/22日本時間上午九點，我在豐岡往京都的JR特急上等待kaggle競賽開獎。由於很早就安排帶小孩出遊，所以最後幾天只用預先準備好的submission玩Fi… ... <看更多>

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The dataset consists of 170,000 scenes capturing the environment around the autonomous vehicle. Each scene encodes the state of the ...

#99. real-time object detection for autonomous driving based

on KITTI dataset [10] which is specialized for autonomous driving. We proposed a novel technique called memory map, which considers inter-frame information, ...

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