# P D N: A Priori Dictionary Network for Fashion Parsing
## 3 Experiments
### LIP Dataset
#### 3.3.2 Results  
For the LIP dataset, we used the LIP validation set as the test set because the official dataset does not provide annotation results for the test set. Similar to [45], we used the same evaluation matrices to measure the performance of our model, including pixel accuracy (PA), mean class pixel accuracy (MPA) and mean intersection over union (mIoU). As shown in Table 4, our model achieves the best PA, MPA and mIoU, which are equal to 76.02%, 54.46% and 45.12%, respectively. Due to the limited generalization caused by the scarcity of training data, the accuracy of PDN-3K noticeably decreased with low-data scenario training. However, it can be found that the evaluation results are still close to those of DeepLabv3+. Overall, our model still achieves the average performance level of segmentation algorithms, indicating that our proposed model exhibits good adaptability, even under extreme conditions. Additionally, the IoU metrics for each category are listed in Table 5. Our PDN model shows a more obvious improvement in upper clothes and skirts, and this is attributed to our proposed clothing priori dictionary module.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **PA** & **MPA** & **mIoU** \\ \hline PSPNet [43] & 74.95 & 54.20 & 44.22 \\ DeepLabv3+ [42] & 73.70 & 51.19 & 36.76 \\ DANet [41] & 72.06 & 54.11 & 36.51 \\ DTPR [6] & 74.10 & 45.92 & 44.29 \\ PDN-3K (ours) & 68.41 & 48.08 & 35.24 \\ PDN (ours) & 76.02 & 54.46 & 45.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of experiments on LIP dataset.  
In Figure 9, we present a subset of the testing results on the LIP dataset, which are arranged from left to right as follows: the original images from the dataset, the results of PDN-3K, the results of the PDN and the ground truth. It can be observed that although there are some prediction errors in the PDN-3k model for categories such as Skirt and Shoe, the overall segmentation shape remains close to the ground truth, indicating that our model still exhibits good stability even in low-data scenarios. Moreover, the training results on the entire dataset closely approximate the ground truth across all categories, demonstrating that the proposed priori dictionary module can assist the model in capturing more accurate clothing details.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **PSPNet** & **DeepLabv3+** & **DANet** & **PDN** \\ \hline Hat & 55.08 & 52.02 & 57.08 & 57.35 \\ Hair & 66.41 & 62.75 & 67.33 & 68.52 \\ Glove & 34.14 & 33.60 & 36.85 & 37.56 \\ Sunglasses & 31.49 & 26.06 & 31.17 & 31.69 \\ Upper Clothes & 65.59 & 64.32 & 65.77 & 67.85 \\ Dress & 20.77 & 20.37 & 18.66 & 19.64 \\ Coat & 51.41 & 49.34 & 50.92 & 52.17 \\ Socks & 40.43 & 37.68 & 41.23 & 39.39 \\ Pants & 71.05 & 72.70 & 71.27 & 71.19 \\ Jumpsuits & 26.96 & 27.94 & 28.14 & 29.56 \\ Scarf & 12.88 & 13.89 & 15.83 & 16.23 \\ Skirt & 15.59 & 14.64 & 16.09 & 17.54 \\ Face & 78.03 & 74.10 & 79.07 & 78.62 \\ Left arm & 57.84 & 59.73 & 59.15 & 60.09 \\ Right arm & 61.43 & 61.97 & 62.47 & 62.75 \\ Left leg & 47.93 & 48.36 & 47.90 & 48.03 \\ Right leg & 49.76 & 49.26 & 49.89 & 49.57 \\ Left shoe & 29.27 & 28.03 & 29.67 & 30.85 \\ Right shoe & 28.28 & 29.59 & 29.23 & 29.08 \\ Avg & 42.23 & 41.32 & 42.86 & 43.41 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Per-class IOU on the LIP dataset.  
Figure 9: Testing samples of PDN on LIP dataset.