# Human Attribute Recognition--
## 4 Datasets
### Synthetic Datasets  
Virtual reality systems and synthetic image generation have become prevalent in the last few years, and their results are more and more realistic and of high resolution. Therefore, we also discuss some data sources comprising computer-generated images. It is a well-known fact that the performance of deep learning methods is highly dependent on the amount and distribution of data they were trained on, and synthetic datasets could theoretically be used as an inexhaustible source of diverse and balanced data. In theory, any combination of attributes in any amount could be synthetically generated. * DeepFashion--Fashion Image Synthesis. The authors of DeepFashion [91] introduce FashionGAN, an adversarial network for generating clothing images on a wearer [171]. FashionGAN is organized into two stages: on a first level, the network generates a semantic segmentation map modeling the wearer's pose. In the second level, a generative model renders an image with \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset Type** & **Dataset** & **\#images** & **Demographic** & **Accessories** & **Appearance** & **Clothing** & **Colour** & **Setup** \\ \hline \multirow{6}{*}{Pedestrian} & PETA [152] & 19,000 & ✓ & ✓ & ✓ & ✓ & ✓ & 10 databases \\ \cline{2-8}  & RAP v1 [153] & 41,585 & ✓ & ✓ & ✓ & ✓ & ✓ & indoor static camera \\ \cline{2-8}  & RAP v2 [108] & 84,928 & ✓ & ✓ & ✓ & ✓ & ✓ & indoor static camera \\ \cline{2-8}  & DukeMTMC \({}^{\ddagger}\) & 34,183 & ✓ & ✓ & ✗ & ✓ & ✓ & outdoor static camera \\ \cline{2-8}  & PA-100K [88] & 100,000 & ✓ & ✓ & ✗ & ✓ & ✗ & outdoor, surveillance cameras \\ \cline{2-8}  & Market+1501 [24] & 1501 & ✓ & ✓ & ✓ & ✓ & ✓ & outdoor \\ \cline{2-8}  & P-DISTRE [160] & 14M & ✓ & ✓ & ✓ & ✓ & ✓ & UAV \\ \hline \multirow{6}{*}{Full body} & Parse27k [161] & 27,000 & ✓ & ✓ & ✗ & ✗ & ✗ & outdoor moving camera \\ \cline{2-8}  & CRP [162] & 27,454 & ✓ & ✓ & ✗ & ✗ & ✗ & moving vehicle \\ \cline{2-8}  & APS [166] & 3661 & ✓ & ✓ & ✓ & ✓ & ✓ & 3 databases \\ \cline{2-8}  & HAT [66] & 9344 & ✓ & ✓ & ✗ & ✓ & ✗ & Flickr \\ \cline{2-8}  & CAD [123] & 1856 & ✓ & ✓ & ✗ & ✓ & website crawling \\ \cline{2-8}  & Describing People [68] & 8035 & ✓ & ✓ & ✗ & ✓ & ✗ & 2 databases \\ \cline{2-8}  & WIDER [75] & 13,789 & ✓ & ✓ & ✓ & ✓ & website crawling \\ \hline \multirow{2}{*}{Synthetic} & CTD [169] & 880 & ✗ & ✗ & ✗ & ✓ & ✓ & generated data \\ \cline{2-8}  & CLOTIBD [170] & 2.1M & ✗ & ✗ & ✗ & ✓ & ✓ & generated data \\ \hline \hline \end{tabular}  
\({}^{\ddagger}\) Permanently suspended regarding privacy issues.  
\end{table} 
Table 1: Pedestrian attributes datasets. precise regions and textures conditioned on this map. In this context, the DeepFashion dataset was extended with 78,979 images (taken for the In-shop Clothes Benchmark), associated with several caption sentences and a segmentation map. * CTD Dataset. Clothing Tightness dataset [169] (CTD) comprises 880 3D human models, under various poses, both static and dynamic, \"dressed\" with 228 different outfits. The garments in the dataset are grouped under various categories, such as \"T/long shirt, short/long/down coat, hooded jacket, pants, and skirt/dress, ranging from ultra-tight to puffy\". CTD was gathered in the context of a deep learning method that maps a 3D human scan into a hybrid geometry image. This synthetic dataset has important implications in virtual try-on systems, soft biometrics, and body pose evaluation. The main drawbacks of this dataset are that it cannot capture exaggerated human postures of low 3D human scans. * CLOTH3D Dataset. CLOTH3D [170] comprises thousands of 3D sequences of animated human silhouettes, \"dressed\" with different garments. The dataset features a large variation on the garment shape, fabric, size, and tightness, as well as human pose.