## 4 Object Detection Datasets and Metrics
* CUB-200-2011 [79]: Cub-200 dataset [80] consists of 200 bird species annotated classes, and each class has 11,788 images. Every annotated image has a single bounding box image (annotations per image: 15 part locations, 312 binary attributes, and one bounding box). * Caltech-256 [81]: It contains 256 object classes with a total of 30,607 images for each class 80 images, and is not suitable for object localization. * ILSVRC [82]: Since 2010, the \"ImageNet Large Scale Visual Recognition Challenge (ILSVRC)\" has been conducted every year for object detection and classification. The ILSVRC dataset contains 10 times more object classes than PASCAL VOC. It contains 200 object classes, whereas the PASCAL VOC dataset contains only 20 object classes. * PASCAL VOC [83]: The \"Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL) Visual Object Classes (VOC)\" challenge provides standardized image datasets for object class recognition tasks, and a common set of tools to access available datasets, and enables evaluations and comparisons of the performances of various object detection methods from 2008 to 2012. For object detection, all the researchers mostly follow MS-COCO and PASCAL-VOC datasets. Table 5 gives brief summary of the main stages of PASCAL VOC developments and image dataset statistics [4], and includes new challenges every year. Table 6 shows image classification dataset challenges. Table 7 shows a comparison of object detection datasets between PASCAL VOC and ILSVRC. Some datasets like LabelMe [84], SUN (Scene-understanding) 2012 [85] provide better \"image annotation than image labeling\". LabelMe contains 187k images; by using bounding polygon all objects are annotated. SUN dataset [85] has 900 classes of scenes, and more than 135K images. Compared to ImageNet, Open Images [86] is much larger and contains more than 9 million real-life images with 6000 classes. For autonomous driving, a specific dataset KITTI vision [47] is used and the images are taken from the mid-size city. For agriculture application FieldSAFE [87] dataset is used and it holds around 2 h of raw sensor data collected on a grass moving scenario. The following are the standard metrics used for evaluating the performance of detection algorithms: average precision (AP), mean average precision (mAP), speed (frames per second), true positive, false positive, IOU threshold, recall, and confidence threshold. Mean average precision (mAP) is widely used as a performance evaluation metric for object detection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Classes** & **Images** & **Annotated Objects** & **Segmentation** &
\begin{tabular}{c} **New Developments in Addition to Classification** \\ **and Detection Challenges** \\ \end{tabular} \\ \hline
2008 & 20 & 4340 & 10,363 & nil & occlusion flag added to annotations \\
2009 & 20 & 7054 & 17,218 & 3211 & improved dataset along with new augmented images \\
2010 & 20 & 10,103 & 23,374 & 4203 & classification action introduced based on ImageNet \\
2011 & 20 & 11,530 & 27,450 & 5034 & classification action extended to 10 classes plus others \\
2012 & 20 & 11,530 & 27,450 & 6929 & size of Segmentation dataset substantially increased, annotated with a reference point on the body \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Description of VOC dataset challenges.