MobileNetV3 represents a significant evolution in the MobileNet series, building on the efficiency and lightweight design of its predecessors while pushing performance further. Introduced in the 2019 paper "Searching for MobileNetV3" by Howard et al., the model leverages both architectural innovations and advanced optimization techniques to deliver high accuracy with low latency—making it ideal for mobile and edge devices. The core of MobileNetV3 lies in its modified bottleneck blocks, which are based on the inverted residual structure from MobileNetV2 but enhanced with two key components: the Squeeze-and-Excitation (SE) module and hard activation functions. These additions are not applied uniformly across all blocks—instead, the final architecture is determined through Neural Architecture Search (NAS), a process that automatically identifies the optimal configuration of blocks, including which ones should include SE modules and which activation functions to use. The two variants, MobileNetV3-Large and MobileNetV3-Small, are designed for different use cases. MobileNetV3-Large prioritizes accuracy and is suitable for applications where performance is critical, while MobileNetV3-Small focuses on minimizing computational cost and memory footprint, ideal for constrained environments. Each bottleneck block begins with a pointwise convolution that expands the number of channels (controlled by the expansion size), followed by a depthwise convolution that processes each channel independently. The key difference from MobileNetV2 is the use of hard-swish as the activation function after the first two convolutions, replacing ReLU6. Hard-swish is a computationally efficient approximation of the swish function, which is defined as x * sigmoid(x). By replacing the standard sigmoid with the hard-sigmoid (a piecewise linear function), the model reduces the computational burden significantly, especially on low-power devices. The SE module is inserted after the depthwise convolution. It works by learning channel-wise importance through a global average pooling operation, followed by two fully connected layers with a reduction ratio of 4. The final activation in the SE module is hard-sigmoid, not the standard sigmoid, further reducing computational cost. The output of the SE module is used to scale the input feature map, allowing the network to focus on more relevant channels. A critical design choice in MobileNetV3 is the use of a linear bottleneck in the final pointwise convolution, where no activation function is applied. This aligns with the original MobileNetV2 design, which found that non-linearities in the final layer of the bottleneck can harm performance. The residual connection is only applied when the input and output dimensions match, i.e., when the stride is 1 and the number of input and output channels are equal. The model’s flexibility is further enhanced by two tunable parameters: the width multiplier (ranging from 0.35 to 1.25) and the input resolution (96 to 256). Adjusting the width multiplier reduces the number of channels, shrinking the model size, while increasing the input resolution boosts accuracy at the cost of higher computation. Experimental results show that MobileNetV3 outperforms MobileNetV2 in both accuracy and latency, especially in the large variant. On ImageNet, MobileNetV3-Large achieves the best accuracy among models of similar complexity, despite having a higher parameter count. The small variant also leads its group in accuracy, demonstrating the effectiveness of the design choices. Quantization experiments confirm that the use of hard activations makes the model highly compatible with low-precision inference, leading to faster execution on mobile hardware, though with a small drop in accuracy. In implementation, the model is built by defining reusable components: a custom ConvBlock for standard, depthwise, and pointwise convolutions with optional batch normalization and activation, a custom SEModule with hard-sigmoid, and a Bottleneck class that combines these into a full block. The main MobileNetV3 class is then constructed by stacking these blocks according to the NAS-optimized configuration. The final model, when instantiated with default settings, has approximately 5.5 million parameters—matching the values reported in the original paper and the official PyTorch implementation. The forward pass correctly processes a 224x224 input through the full network, producing a 1000-dimensional output for ImageNet classification. In summary, MobileNetV3 is a well-optimized, highly efficient model that combines the best of NAS, channel attention, and low-precision-friendly activation functions. It stands as a strong example of how careful architectural design and hardware-aware optimization can lead to state-of-the-art performance on resource-constrained devices.