## 6 Experimental Results
### Searching Results via Darts_Tiny  
In this section, we evaluate Darts_Tiny by using HRSID and SSDD. We leverage the super neural network depicted in Figure 7 to perform a neural architecture search and then derive several neural networks with various numbers of channels. The neural architecture search is fixed to 100 epochs. The time costs are around 36 hours (running on 4 Nvidia Tesla V100 GPUs) on average. Experimental results on HRSID are provided in Table 5.  
The given results include the number of channels, the amount of parameters (\"Paras\"), the amount of computation (\"FLOPs\"), AP and F1. The number of channels is given in the form of \"\(x\times 4\)\", as there are 4 internal states (see Figure 5). For each state, channel pruning will cut down half of its channels. The amount of parameters and the amount of computation reflects the scale of a neural network. Generally, fewer parameters and computations lead to higher computation efficiency. The amount of computation is represented by FLOPs including all multiply-and-accumulate calculations. F1 is a popular criterion in object detection. In Darts_Tiny, we mainly focus on F1 to represent the detection accuracy and take AP as a secondary reference. Both F1 and AP synthetically consider precision and recall. We provide a sequence of architectures with different channels generated during one search process. In Table 5, there are eight architectures. The first four are obtained by setting \(ops\_ratio=0\), while the last four are obtained by setting \(ops\_ratio=0.012\). Compared with the results in Tables 2 and 3, all the eight architectures derived by Darts_Tiny have a substantial improvement in detection accuracy. On the other hand, when the number of channels is cut down, both the amount of parameters and computations decrease sharply. Clearly, channel pruning enhances efficiency. From the first four architectures, we can see that as the amount of computation decreases, both AP and F1 present a downward trend. However, by considering the amount of computation saved, an acceptable loss in detection accuracy is reasonable especially in an environment without sufficient computation resources. For example, compared with H_Arch_32, H_Arch_8 only costs 10% of the amount of computation. The last four architectures in Table 5 show something different. H_Arch_C_32 does not have the best performance in terms of F1. When generating the last four architectures, the loss function is composed of the cross-entropy loss and the amount of computations (see Equation (8)). We observe that the architecture search in this case usually needs more epochs to obtain a superior neural network. Therefore, H_Arch_C_32 generated in an early epoch is not as good as H_Arch_C_16 and H_Arch_C_8 generated in later epochs. Finally, by comparing the first four with the last four neural architectures, we can find that integrating Ops in the loss function succeeds in further reducing the number of computations. We conduct the same evaluations by using SSDD to test whether Darts_Tiny can perform consistently in different datasets. Evaluation results are given in Table 6. Overall, Darts_Tiny also works well on SSDD. As channels are cut down, the number of parameters and the number of computations decrease, which enhances computation efficiency. On the other hand, channel pruning does not bring apparent negative impacts on AP and F1.