## 5 Publicly Available Datasets and Benchmarks
### Places  
This dataset comprises around 2.5 million scene images [14] and contains an evaluation criterion based on top-5 error. Considering the case with the SUN-397 dataset, that provides scene images in good numbers, but each category still suffers from sufficient data required for feeding deep learning models. The Places-205 dataset, on the other hand, contains 205 scene categories that are similar with each category containing at least 5000 images for training; 100 and 200 images belonging to each category are utilized for validation and testing. The Places2 dataset [82] is an extension of the Places dataset and combines around 10 million images with scene categories containing more than 400. To date, this dataset seems to be probably more challenging to carry out scene recognition as it is based on the current occurrences of scenes. It is inferred that more than 4000 images are selected for each class across 365 categories for coming up with datasets like Places365-Standard and Places365-Challenge. Table 2, drawn from [11], illustrates the accuracies comparison of some of the scene recognition algorithms with respect to feature extraction. These algorithms are grouped into six categories, as discussed in Section 2. Global attribute descriptors lead to worst recognition accuracy as these descriptors are obtained without training and by some predefined numerical calculation and are not likely to be suggested for the present scene recognition applications. In recent years, there has been extensive exploration of patch feature encoding, driven by its recognition accuracy, which shows similarity with methods such as patch features and codebook learning. As seen in Table 2, the features retrieved from patches achieve a recognition accuracy that is higher than the handcrafted features. However, advanced codebook learning methods may lead to better outputs due to their inherent relationships. An advantage of patch feature encoding is that these algorithms are trained to deal with cluttered backgrounds and with deformed objects in images to a certain degree. The sports-8 dataset achieves the highest recognition accuracy of 99.10% when compared to the other datasets. Patch feature encoding can be applied in situations where computational resources are limited with limited scene categories, and where the response time is of paramount importance compared to the accuracy of scene recognition. Spatial layout pattern (SLP) learning improves the recognition accuracy of the scene, but an excessive number of spatial partitions can negatively impact scene recognition accuracy. Consequently, while SLP learning proves effective for stable indoor and outdoor scenes, it may cause confusion in recognizing indoor scenes (that are highly lookalike) with similar spatial layouts. These algorithms achieve moderate recognition accuracy with some minor changes to the existing CNNs and require additional computations in comparison to the current frameworks and utilize minimal inference time. Discriminative region detection addresses the shortcomings of extreme spatial partitions in context to SLP learning and focuses on the detection of regions of interest (Rols) in complex scene categories and achieves better recognition results as seen in Table 2. To detect the RoIs, some algorithms make use of pretrained object detectors such as Object Bank [70], where the training process is more arduous and consumes a lot of time and increases the computational overhead. Also, the detection of discriminative regions is more sensitive to scene categories, and it is obviousthat for larger datasets there are larger objects with many different categories. In general, these kinds of discriminative detection algorithms achieve recognition accuracies that are higher even on smaller and intermediate datasets in a reasonably shorter time frames.