GoogLeNet is a convolutional neural network model introduced by Google in 2014, famous for its Inception architecture. It won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2014). The core of GoogLeNet is the Inception module, which performs multiple convolutions and pooling operations in parallel at the same layer, capturing multi-scale features of images while reducing the number of parameters and computational complexity. Compared to traditional convolutional neural networks, GoogLeNet has greater depth but achieves higher computational efficiency due to its modular design. Consisting of 22 layers with several Inception modules, GoogLeNet is widely used in tasks like image classification and object detection.

Source model

• Input shape: 224x224
• Number of parameters: 6.31M
• Model size: 25.29M
• Output shape: 1x1000

Source model repository: GoogLeNet

When the user has fine-tuned the source model, the model conversion process must be performed again.

Users can refer to either of the following two methods to complete the model conversion:

• Using AIMO for model conversion: Click Model Conversion Reference in the Performance Reference section on the right to view the conversion steps.

• Using Qualcomm QNN for model conversion: Please refer to the Qualcomm QNN Documentation.

The model performance benchmarks and example code provided by Model Farm are all implemented based on the APLUX AidLite SDK.

For models in .bin format, you can use either of the following two inference engines to run inference on Qualcomm chips:

Inference using APLUX AidLite: please refer to the APLUX AidLite Developer Documentation

Inference using Qualcomm QNN: Please refer to the Qualcomm QNN Documentation

Inference Example Code

The inference example code is implemented using the AidLite SDK.

Click Model & Code to download the model files and the inference code package. The file structure is as follows:

/model_farm_{model_name}_aidlite
    
    |__ models # folder where model files are stored

    |__ python # aidlite python model inference example

    |__ cpp # aidlite cpp model inference example

    |__ README.md