Mobilenet V2 Architecture in Computer Vision
Last Updated : 17 Jun, 2024
MobileNet V2 is a highly efficient convolutional neural network architecture designed for mobile and embedded vision applications. Developed by researchers at Google, MobileNet V2 improves upon its predecessor, MobileNet V1, by providing better accuracy and reduced computational complexity.
This article delves into the key features, architecture, and advantages of MobileNet V2, making it an essential read for anyone interested in lightweight and efficient neural networks.
Background of MobileNet V2 Architecture
The need for efficient neural network architectures has grown with the proliferation of mobile devices and the demand for on-device AI applications. Traditional deep learning models are computationally expensive and require significant memory, making them unsuitable for deployment on resource-constrained devices. MobileNet V2 addresses these challenges by introducing an optimized architecture that balances performance and efficiency.
Key Features of MobileNet V2
1. Inverted Residuals
MobileNet V2 introduces the concept of inverted residuals with linear bottlenecks. This approach preserves the input and output dimensions while performing the intermediate layers in a lower-dimensional space, reducing the computational cost. The inverted residual block consists of three layers:
- 1x1 Convolution (Expansion Layer): Expands the input channels by a factor, increasing the dimensionality of the data.
- Depthwise Convolution: Applies a depthwise convolution to each expanded channel independently, performing spatial convolution.
- 1x1 Convolution (Projection Layer): Projects the expanded data back to a lower-dimensional space, reducing the number of channels to the desired output size.
2. Depthwise Separable Convolutions
Similar to MobileNet V1, MobileNet V2 utilizes depthwise separable convolutions, which split a standard convolution into two operations: depthwise convolution and pointwise convolution. This separation significantly reduces the number of parameters and computations, making the network more efficient.
3. Linear Bottlenecks
The architecture incorporates linear bottlenecks between layers, ensuring that the manifold of the input data is not overly compressed. This technique helps in retaining more information and improving model accuracy. The linear bottleneck layer follows the pattern of 1x1 convolution for expansion, depthwise convolution for spatial filtering, and another 1x1 convolution for projection.
4. ReLU6 Activation Function
MobileNet V2 employs the ReLU6 activation function, a modified version of the ReLU function. ReLU6 restricts the activation values to a range of [0, 6], providing better quantization properties for efficient computation on mobile devices. This activation function helps in achieving a balance between accuracy and efficiency.
MobileNet V2 Architecture
The MobileNet V2 architecture is built upon several key building blocks, including the inverted residual block, which is the core component of the network.
Here’s a detailed look at the architecture:
Network Structure
MobileNet V2 follows a streamlined architecture consisting of:
- Initial Convolution Layer: A standard convolution layer with 32 filters and a stride of 2.
- Series of Inverted Residual Blocks: The network contains several stages, each with a specific number of inverted residual blocks. The expansion factors, output channels, and strides vary across stages to manage the computational complexity and receptive field.
- Final Convolution Layer: A 1x1 convolution layer with 1280 filters, followed by a global average pooling layer.
- Fully Connected Layer: A fully connected layer with softmax activation for classification tasks.
Detailed Layer Configuration
Here’s a detailed breakdown of the layer configuration for MobileNet V2:
| Layer Type | Input Size | Output Size | Kernel Size | Stride | Expansion Factor |
|---|
| Initial Conv | 224x224x3 | 112x112x32 | 3x3 | 2 | - |
| Inverted Residual Block | 112x112x32 | 112x112x16 | 3x3 | 1 | 1 |
| Inverted Residual Block x2 | 112x112x16 | 56x56x24 | 3x3 | 2 | 6 |
| Inverted Residual Block x3 | 56x56x24 | 28x28x32 | 3x3 | 2 | 6 |
| Inverted Residual Block x4 | 28x28x32 | 14x14x64 | 3x3 | 2 | 6 |
| Inverted Residual Block x3 | 14x14x64 | 14x14x96 | 3x3 | 1 | 6 |
| Inverted Residual Block x3 | 14x14x96 | 7x7x160 | 3x3 | 2 | 6 |
| Inverted Residual Block x1 | 7x7x160 | 7x7x320 | 3x3 | 1 | 6 |
| Final Conv | 7x7x320 | 7x7x1280 | 1x1 | 1 | - |
| Global Avg Pooling | 7x7x1280 | 1x1x1280 | - | - | - |
| Fully Connected | 1x1x1280 | 1x1x1000 | - | - | - |
Implementing MobileNet V2 using TensorFlow
Here’s an example of how to implement MobileNet V2 using TensorFlow. For this implementation, we have used cat image.
Python import tensorflow as tf from tensorflow.keras.applications import MobileNetV2 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions import numpy as np # Load the MobileNetV2 model model = MobileNetV2(weights='imagenet') # Load an image for testing img_path = '/content/simba-8618301_1280.jpg' # Path to your test image img = image.load_img(img_path, target_size=(224, 224)) # Preprocess the image x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) # Make predictions preds = model.predict(x) print('Predicted:', decode_predictions(preds, top=3)[0]) Output:
Predicted: [('n02123045', 'tabby', 0.5783735), ('n02123159', 'tiger_cat', 0.11342117), ('n02124075', 'Egyptian_cat', 0.05013833)]The output of the prediction made by the MobileNet V2 model on the test image is a list of tuples. Each tuple contains three elements:
- Class ID: A unique identifier for the predicted class.
- Class Name: The human-readable label for the predicted class.
- Probability Score: The confidence level of the model for that prediction, expressed as a probability.
Interpretation
- Highest Confidence Prediction: The model is most confident that the image is of a tabby cat, with a probability score of 0.5783735. This means that out of all possible classes, the model believes the image most likely belongs to the "tabby" class.
- Next Best Predictions: The model also considers the image might belong to the "tiger_cat" or "Egyptian_cat" classes, but with lower confidence scores.
Advantages of MobileNet V2
- Efficiency: MobileNet V2 achieves a good balance between accuracy and efficiency, making it ideal for mobile and embedded applications.
- Flexibility: The architecture can be easily scaled to meet the specific needs of different applications by adjusting the width multiplier and resolution multiplier.
- Improved Performance: Compared to its predecessor, MobileNet V2 provides better performance with fewer parameters and lower computational cost.
Applications of MobileNet V2
MobileNet V2 is well-suited for a variety of applications, including:
- Image Classification: Efficiently classifying images on mobile devices with limited computational resources.
- Object Detection: Serving as a backbone for lightweight object detection models.
- Semantic Segmentation: Enabling real-time segmentation tasks on resource-constrained devices.
- Embedded Vision: Powering vision-based applications in embedded systems, such as drones, robots, and IoT devices.
Conclusion
MobileNet V2 is a powerful and efficient neural network architecture designed for mobile and embedded applications. Its innovative design, featuring inverted residuals and linear bottlenecks, enables high performance with low computational requirements. Whether for image classification, object detection, or other vision-based tasks, MobileNet V2 provides a robust solution for deploying AI on resource-constrained devices.
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