MobileNet V3 is an evolution of the MobileNet architecture designed specifically for efficient image classification on mobile and embedded devices. This architecture combines the advantages of MobileNet V1 and V2 and integrates advanced techniques to improve both accuracy and efficiency and currently represents a significant advance in the development of efficient neural networks optimized specifically for use on mobile and resource-constrained devices. By combining modern techniques such as SE modules, modified activation functions and advanced architecture searches, MobileNet V3 offers outstanding performance with minimal computational overhead. This makes it an ideal choice for a wide range of applications in mobile and embedded AI.
Architectural features
- Inverted Residuals with Linear Bottleneck:
- Already introduced in MobileNet V2, this technique forms the basis for MobileNet V3 and consists of inverted residual blocks that reduce the number of computations and parameters while maintaining model accuracy.
- Squeeze-and-Excitation (SE) Modules:
- SE modules have been integrated into MobileNet V3 to improve channel selection. These modules perform an adaptive weighting of the channels, which increases the representational capability of the network.
- Lightweight Building Blocks:
- MobileNet V3 uses specialized building blocks that are optimized for efficiency. These include adapted kernels, optimized activation functions and batch normalizations.
Technical innovations
- Network Search and NAS (Neural Architecture Search):
- Google has relied heavily on Neural Architecture Search in the development of MobileNet V3 to find the optimal structure of the networks. This helps to optimize the balance between model size, speed and accuracy.
- Modified Hard-Swish Activation:
- Instead of the classic ReLU activation function, MobileNet V3 uses a modified hard-swish function. This activation function improves the efficiency and accuracy of the network.
- Efficient Last Stage Design:
- The last stage of the network has been optimized to maximize classification performance while minimizing complexity. A combination of pooling and fully connected layers is used here.
Applications and areas of use
MobileNet V3 is particularly well suited to applications that need to run on mobile and embedded devices as it offers a good balance between computational complexity and accuracy. Here are some typical application areas:
- Image and video recognition:
- MobileNet V3 is often used in apps that require real-time image recognition and classification, such as augmented reality (AR) or security applications.
- Object recognition:
- Thanks to its efficient architecture, MobileNet V3 is also ideal for real-time object detection, e.g. in autonomous vehicles or for surveillance.
- Voice and gesture recognition:
- The model architecture can also be adapted for non-visual tasks, such as recognizing voice commands or hand gestures.
Benchmarks
The average inference time is a critical performance indicator for deep learning models, especially in real-time applications. The seemingly slower GPU can be faster in practice if it is better optimized for the specific workloads, offers lower latency, works more efficiently with certain data formats or benefits from better driver and software support. For short compute times, the latency caused by initialization and communication between the GPU and CPU can have a greater impact than pure computing power. GPUs that are better at minimizing these latencies can therefore work more effectively. Some GPUs are also more thermally and energetically efficient, which means they can maintain their maximum performance over longer periods of time without throttling.
35 Antworten
Kommentar
Lade neue Kommentare
Urgestein
Mitglied
Urgestein
Veteran
Urgestein
1
Urgestein
Urgestein
1
Urgestein
1
Urgestein
Veteran
Urgestein
Urgestein
Urgestein
Urgestein
Urgestein
Urgestein
Alle Kommentare lesen unter igor´sLAB Community →