## 4 Object Detection Datasets and Metrics  
Since all CV algorithms are trained and verified using datasets only, datasets play a crucial role in achieving correct outputs. * ImageNet [70]: It is based on WordNet Hierarchy. WordNet is also referred to as Synset. To define each synset, on average 1000 images are provided by ImageNet. ImageNet dataset offers billions of images in WordNet Hierarchy. It has a total of 14,197,122 images--images with bounding box annotations of 1,034,908 and an image resolution of 480 \(\times\) 410 pixels. In ImageNet, to detect local features, 1.2 million images exhibit SIFT (scale-invariant feature transform) features. * WIDERFACE [71]: This dataset contains total of 32,202 images and which includes around 400,000 faces for a wide range of scales. The dataset is split into three parts: training data 40%, validation data 10%, and testing data 50%. * FDDB [72]: \"Face Detection Dataset and Benchmark\" contains 2845 images with a total of 5171 faces. Since it is a small dataset, it is generally used for testing only, and WIDERFACE dataset is used for training the object detector. * CityPersons [73]: It is a newly created and challenging pedestrian dataset on the top of the Cityscapes [74] dataset. This dataset is very useful, especially in more difficult cases, such as with small-scale data and heavily occluded pedestrians. It contains 5000 images that were captured from various cities in Germany. * INRIA [13]: INRIA is a popular person dataset used in pedestrian detection. It contains 614 person images for training and 288 person images for testing. * KITTI [47]: This dataset contains high resolution 7481 labeled images and 7518 testing images. \"Person class\" in this dataset is divided into two subclasses--pedestrian and cyclist, and it is tested on three evaluation metrics--easy (E), moderate (M) and hard (H). * ETH [75]: This dataset contains three videoclips and which have a total of 1804 frames, and it is commonly used as a testing dataset. * 80M Tiny image dataset [76]: This dataset contains around 80 million 32 \(\times\) 32 colored images. * Microsoft COCO (MS-COCO) [77]: \"Microsoft Common Objects in Context\" dataset has 330,000 images comprising 250,000 labeled images; 150 are object instances, 80 are object categories, 91 are stuff categories, and 5 are captions per image. This dataset exhibits features such as context recognition, multi-objects per image, and object segmentation. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Name** & **Services** & **Features** & **Accessibility** \\ \hline Microsoft Cognitive Service [65] & computer vision & Face, vision and speech recognition, object motion tracking, facial expression recognition, speech understanding, image tagging, language understanding. & REST API \\ \hline Amazon Rekognition [66] & image recognition & Activity, scene and object detection, facial recognition on images and video, text in images, detect unsafe video. & HTTP \\ \hline IBM Watson Vision Recognition Service [67] & understanding content in the images & Train a custom model for visual inspection, face detection, image class description and taxonomy, image matching identification and, supports multi-languages. & HTTP \\ \hline Google Cloud Vision [68] & image analysis & Content detection, face, landmark and logo detection, image sentiment analysis. & Integrated REST API \\ \hline Cloud Sight [69] & image understanding & Via REST API when an image is forwarded, then the response is given as image Description. & REST API \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of available API services for object detection. * CIFAR-100 dataset [78]: This dataset provides only 100 object classes; each contains 600 images of which 500 and 100 per class are training and testing images; 100 object classes are clustered into 20 superclasses, and each image comes with a \"fine\" label (the class to which it belongs) and a \"coarse\" label (the superclass to which it belongs).