# Evaluating DL Model Scaling Trade-Offs During Inference via an Empirical Benchmark Analysis
## 4 DL Use Cases and Validation Datasets
### Object Detection and Classification  
This use case centers on object detection, where the DL application setup is configured to handle a series of inference tasks to detect objects within a set of given images and classify the objects by assigning appropriate labels from a pre-defined label set. The chosen model architecture for this purpose is the widely used EfficientNet convolutional neural network [13]. EfficientNet was first introduced in 2020 by GoogleAI to provide object detection for mobile and IoT services run at the network edge. For EfficientNet, the baseline network  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DNN Model** & **No. of** & **Scaling** & **Experimental** \\
**Architecture** & **Variants** & **Dimensions** & **Value Sets** \\ \hline \multirow{2}{*}{BERT} & \multirow{2}{*}{20} & Network depth (D); & D = {2, 4, 6, 8, 10}, \\  & & Hidden embeddings (H) & H = {128, 256, 512, 768} \\ \hline \multirow{4}{*}{EfficientNet} & \multirow{4}{*}{6} & Network depth (D); & D = {1.0, 1.1, 1.2, 1.4, 1.8, 2.2}, \\  & & Network width (W); & W = {1.0, 1.0, 1.1, 1.2, 1.4, 1.6}, \\  & & Input resolution (R); & R = {224, 240, 260, 300, 380, 456}, \\  & & Dropout rate (O) & O = {0.2, 0.2, 0.4, 0.4, 0.6, 0.6} \\ \hline \multirow{4}{*}{MLP} & \multirow{4}{*}{120} & Network depth (D); & D = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, \\  & & Network width (W); & W = {32, 64, 128}, \\ \cline{1-1}  & & Batch size (B); & B = {32, 64, 128, 256}, \\ \cline{1-1}  & & Epochs (E) & E = {1, 3, 5} \\ \hline \hline \end{tabular} For EfficientNet, values are expressed as coefficients from the baseline \(B_{O}\) model and the \(i_{th}\) value of each dimension was paired together to produce the 6 variants.  
\end{table}
Table 1: DL DNN model architectures and scaling dimensionalities for DL use cases.  
(denoted as \(B_{0}\)) exploits a multi-objective neural architecture search that optimizes for both accuracy and FLOPS by adopting a hierarchy of mobile inverted bottleneck convolution layers and the use of squeeze-and-excitation optimization to ultimately enhance representational capacity [29]. However, the most popular feature of EfficientNet is the introduction of compound scaling, where the network structure can uniformly scale in depth, width, and resolution by taking advantage of a fixed set of scaling coefficients tailored to increase the network's computational efficiency without impacting representational accuracy.  
The publicly released variants of EfficientNet are code-named as Bx, with \(x\) denoting the model complexity rank (\(B_{0}-B_{7}\)) and each varying in network depth, width, resolution, and dropout rate. For this empirical study, we utilize the first six models (\(B_{0}-B_{5}\)), which are all openly available in the Keras pre-trained model library ([[https://keras.io/api/applications/efficientnet/](https://keras.io/api/applications/efficientnet/)]([https://keras.io/api/applications/efficientnet/](https://keras.io/api/applications/efficientnet/)) accessed on 7 November 2024). Hence, we note that, as shown in Table 1, for EfficientNet, the value of each dimension is expressed as a coefficient factor of the base variant (\(B_{0}\)), and the six variants are formed by taking the \(i_{th}\) value from each dimension to form its dimensional value set. As an example, \(B_{4}\) takes values of \(D=1.8\), \(W=1.4\), \(R=380\), and \(O=0.6\). For the inference workload, we use images extracted from the well-known and publicly available Tiny ImageNet dataset hosted on Hugging Face ([[https://huggingface.co/datasets/zh-plus/tiny-imagenet](https://huggingface.co/datasets/zh-plus/tiny-imagenet)]([https://huggingface.co/datasets/zh-plus/tiny-imagenet](https://huggingface.co/datasets/zh-plus/tiny-imagenet)) accessed on 7 November 2024), which includes a validation set of 10,000 color images (64 \(\times\) 64 pixels) categorized into 200 label classes.