在看目前檢測、識別方面的論文時，經常遇到VOC 2007 或者 VOC 2012數據集。為了對這個數據集有一個詳細的了解，專門讀了相關文檔并將一些要點概括如下：

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The PASCAL Visual Object Classes Challenge （2012）

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The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes. There are twenty object classes.

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There are five main tasks. We only focus on three of them: classification, detection, and segmentation.

Classification: For each of the classes, predict the presence/absence of at least one object of that class in a test image.

Detection:?For each of the classes, predict the bounding boxes of each object of that class in a test image (if any).

Segmentation:?For each pixel in a test image, predict the class of the object containing that pixel or "background" if the pixel does not belong to one of the twenty specified classes.

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下面以圖像識別為例進行詳細說明

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Classi cation/Detection Image Sets

For the classification and detection tasks, there are four sets of images provided:

train: Training data

val: Validation data

trainval: The union of train and val

test: Test data

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Classi cation Task

For each of the twenty object classes, predict the presence/absence of at least one object of that class in a test image. The output from your system should be a real-valued confidence of the object's presence so that a precision/recall curve can be drawn.?Participants may choose to tackle all, or any subset of object?classes, for example “cars only” or “motorbikes and cars”.

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Two competitions are defined according to the choice of training data: (i) taken?from the VOC trainval data provided, or (ii) from any source excluding the?VOC test data provided.

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A separate text file of results should be generated for each competition (1 or?2) and each class e.g. ‘car’.?Each line should contain a single identifier and the?confidence output by the classifier, separated by a space, for example:

comp1_cls_test_car.txt:

...

2009_000001 0.056313

2009_000002 0.127031

2009_000009 0.287153

...

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The classification task will be judged by the precision/recall curve.?The principal?quantitative measure used will be the average precision (AP).

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這個賽事主辦方提供了評估性能的函數，我們只需要按照要求輸出文本即可，評估可以直接調用賽事主辦方的API。

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總結

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