## 5 Experimental Results  
### Setup  
**Datasets:** We have used two benchmark re-ID datasets including Market-1501 [39] and DukeMTMCreID [40]; the brief descriptions of these datasets are given below  
* _Market-1501_ ([[http://zheng-lab.eccs.anu.edu.au/Project/project_reid.html](http://zheng-lab.eccs.anu.edu.au/Project/project_reid.html)]([http://zheng-lab.eccs.anu.edu.au/Project/project_reid.html](http://zheng-lab.eccs.anu.edu.au/Project/project_reid.html)), accessed on 18 August 2020) dataset [39] is developed by employing six cameras, including one low and five high-resolution cameras outside a supermarket at Tsinghua University where the field-of-view overlap exists between the different cameras. Market-1501  
Figure 5: The structure of DenseNet (**a**) and Dense Transformer (**b**). The transformer modules are kept at the network’s end instead of each block in the Dense Transformer.  
Figure 6: The network structure of the PCB Transformer, which employs the Residual Transformer as a backbone.  
has 32,668 annotated bounding boxes of 1501 pedestrians. For performing the cross-camera search, each pedestrian is captured by all cameras, while it is ensured that a pedestrian is present in at least two cameras.
* _DukeMTMC-reID_ ([[https://github.com/sxzrt/DukeMTMC-reID_evaluation/download-dataset](https://github.com/sxzrt/DukeMTMC-reID_evaluation/download-dataset)]([https://github.com/sxzrt/DukeMTMC-reID_evaluation/download-dataset](https://github.com/sxzrt/DukeMTMC-reID_evaluation/download-dataset)), accessed on 25 August 2020) dataset is constructed from the DukeMTMC [40] dataset, which consists of high-resolution videos acquired by eight cameras with pedestrian annotated bounding boxes. In [40], the pedestrian images are cropped after each 120 th frame, yielding 1812 identities having 36,411 bounding boxes. Only 702 IDs are select for training, and 702 IDs are selected for testing, making sure that the pedestrians appear in more than two cameras.  
**Baselines:** We compare against three baselines: ResNet50 [23], DenseNet121 [36] and PCB [37]. These methods are fine-tuned using the benchmark datasets, and their results are used as baselines.  
**Evaluation Metrics:** Two widely used evaluation metrics are employed to evaluate the person re-ID predictions, including mean Average Precision, \"mAP\" and Accuracy \"Acc\". Top-1 accuracy is expressed as Rank-1 (R@1), the conventional accuracy where the model outputs the highest probability for the input identity. Top-5 accuracy represented as Rank-5 (R@5) means that any of the five highest probability identities must match the ground truth identity, and top-10 accuracy (R@10) is where the ground truth is present in the top 10 probabilities.  
**Implementation Details:** We use the pre-train weights of ImageNet [41] for convolutional layers and set the batch size to be 16 for training the proposed models with 59 epochs. Stochastic gradient descent (SGD) optimizes the pre-trained model with a momentum of 0.9 and a base learning rate of 0.02, halved after every 20 epochs. We train our proposed models using the PyTorch framework on a PC with V100 GPUs. The time duration for each model varied based on the number of transformers employed. The input size of the image is 256 \(\times\) 128 for Residual-Transformer and Dense-Transformer while 384 \(\times\) 192 for PCB-Transformer. The minimum number of transformers used in the base model is one, while the maximum is 20.  
**Objective Function:** The loss function is the conventional cross-entropy. To investigate the transformer's ability whether it can learn the discriminative features, we have not experimented with more losses intentionally.