ConvNeXt-Tiny
Image Classification
W8A16
INT8
FP16
post
ConvNeXt-Tiny: Image Classification

ConvNeXt-Tiny is the lightweight version of the ConvNeXt model family, a modern convolutional neural network (CNN) designed to revamp traditional CNNs to compete with current popular Transformer models. ConvNeXt-Tiny retains the advantages of convolutional networks while incorporating design ideas from vision Transformers, such as deeper networks, larger convolution kernels, and LayerNorm. Compared to other models, ConvNeXt-Tiny has fewer parameters and lower computational demands but still provides efficient image classification performance, making it particularly suitable for resource-constrained environments like mobile devices or edge computing.

Source model

  • Input shape: 224x224
  • Number of parameters: 27.26M
  • Model size: 109.18M
  • Output shape: 1x1000

Source model repository: ConvNeXt-Tiny

Performance Reference

Device

Backend
Precision
Inference Time
Accuracy Loss
File Size
Model Optimization

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.

Model Inference

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
License
Source Model:BSD-3-CLAUSE
Deployable Model:APLUX-MODEL-FARM-LICENSE
Performance Reference

Device

Backend
Precision
Inference Time
Accuracy Loss
File Size