Model quantization is widely used for fast inference and deployment of deep neural network models. Post-training quantization has attracted much attention from researchers due to its reduced retraining time and low performance loss. However, most existing post-training quantization methods rely on theoretical assumptions or use fixed bit-width allocations for network layers during the quantization process, which results in significant performance loss in the quantized network, especially in low-bit scenarios. To improve the accuracy of post-training quantized network models, this study proposes a novel post-training mixed-accuracy quantization method (MSQ). This method estimates the accuracy of each layer of the network by inserting a task predictor module, which incorporates the pyramid pooling module and weight imprinting, after each layer of the network model. With the estimations, it assesses the importance of each layer of the network and determines the quantization bit-width of each layer based on the assessment. Experiments show that the MSQ algorithm proposed in this study outperforms some existing mixed-accuracy quantization methods on several popular network architectures, and the quantized network model tested on edge hardware devices shows better performance and lower latency.