This work evaluates the compression techniques on ConvNeXt models in image classification tasks using the CIFAR-10 dataset. Structured pruning, unstructured pruning, and dynamic quantization methods are evaluated to reduce model size and computational complexity while maintaining accuracy. The experiments, conducted on cloud-based platforms and edge device, assess the performance of these techniques. Results show significant reductions in model size, with up to 75% reduction achieved using structured pruning techniques. Additionally, dynamic quantization achieves a reduction of up to 95% in the number of parameters. Fine-tuned models exhibit improved compression performance, indicating the benefits of pre-training in conjunction with compression techniques. Unstructured pruning methods reveal trends in accuracy and compression, with limited reductions in computational complexity. The combination of OTOV3 pruning and dynamic quantization further enhances compression performance, resulting 89.7% reduction in size, 95% reduction with number of parameters and MACs, and 3.8% increase with accuracy. The deployment of the final compressed model on edge device demonstrates high accuracy 92.5% and low inference time 20 ms, validating the effectiveness of compression techniques for real-world edge computing applications.

本研究解决了在图像分类任务中卷积神经网络模型的压缩问题。通过评估结构化剪枝、非结构化剪枝和动态量化等技术，发现结构化剪枝能够使模型大小减少多达75%，而动态量化则可减少参数数量高达95%。最终压缩模型在边缘设备上的部署表现出92.5%的高准确率和20毫秒的低推理时间，验证了压缩技术在实际边缘计算应用中的有效性。

边缘人工智能：卷积神经网络模型压缩技术评估