# An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification
## 4 Experiments
### Results
** The complexity of ReID model, affecting the training and computational cost, plays a vital role in the edge deployment. From the last column of Table 4, it can be observed that MSFANet outperforms most published methods about the model's complexity by a clear margin. The parameters and GFLOPs of MSFANet are calculated on resized input images by 256*128, and we can see that the GFLOPs is only 0.82 when the model's backbone is '1-2-3' form, decreased by 18.2% from OSNet [27]. This design verifies the effectiveness of the multi-scale focusing attention network for ReID by applying a depthwise separable convolution mechanism. However, as the backbone form is set as '2-2-2', the GFLOPs will be increased to 0.94, almost equal to OSNet's. Thus, MSFANet's backbone's structural design also plays an important role in the model's computational complexity. Compared to other models, MSFANet is a lightweight model, with significantly smaller parameters and floating-point computations than other heavyweight models (such as ResNet for the backbone). It performs better even compared with OSNet, regarded as a state-of-the-art lightweight ReID model by far. * **Model Accuracy analysis. ** It can be seen from the data in Table 4 that the Rank-1 and mAP of MSFANet and other state-of-the-art methods on Market1501 and DukeMTMC datasets are calculated. The top half of the table, training model from scratch, show that the mAP has increased by 1% and 1.3% when the backbone's structures are '1-2-3' and '2-2-2'. For the bottom half table, training with ImageNet pre-training, the mAP of MSFANet employing the two backbone structures is 86.8% and 86.9%, respectively, huge improvements over OSNet. The results obtained from the preliminary analysis of MSFANet's high efficiency are shown in Table 4. The designs of MSFANet contain multi-scale receptive fields, Omni-scale feature learning, and channel-level attention mechanism, which can capture much more robust and meticulous ReID features. MSFANet uses fewer branches and basic modules, integrating the attention module, which helps speed up the inference of ReID. Overall, these results in Table 4 indicate that MSFANet achieves a better trade-off between model lightweight and model inference capabilities than other lightweight models (such as OSNet, ShuffleNet, and MobileNet, etc.). To some extent, it saves a lot of memory and computing resources on account of the high performance of MSFANet. **Results on small ReID datasets. ** GRID and VIPeR are two tiny ReID datasets. There are only a few hundred training images. Training a ReID model from scratch on these two datasets is a considerable challenge. For a fair comparison, we use transfer learning like other methods. MSFANet uses the pre-trained weights on the Market1501 to perform fine-tuning on these two datasets. As shown in Table 5, compared with OSNet, we can observe that the Rank-1 and mAP of MSFANet with our new data augmentation method increase 10.1% and 10% on VIPeR datasets, respectively. On GRID dataset, MSFANet's mAP is increasing by 0.8%. When our model adopts the same data enhancement method as OSNet, as shown in the penultimate row of Table 5, the mAP and Rank-1 indicators are also greatly improved. Thus, MSFANet also has better performance on small ReID datasets.