Abstract: To enhance the performance of indoor localization using channel state information (CSI) fingerprints, an CSI image-based indoor localization method based on the improved MixNet model is proposed. In the offline phase, the method involves selecting the three access points (APs) with the highest received signal strength indication (RSSI) at the reference point (RP), extracting their CSI data, and converting it into image. Subsequently, the improved MixNet model is employed to train on these images and save the model. The improved MixNet model introduces coordinate attention (CA) and residual connections. Specifically, it replaces the squeeze-and-excitation (SE) attention in MixNet-s with CA to enhance the network’s information representation capability and extract CSI image fingerprint features more accurately. Moreover, it incorporates residual connections, tailored to the characteristics of the MixNet-s model, to enhance the network’s representation capacity and prevent overfitting. Finally, the network depth is reduced to ensure that all network layers are adequately trained. During the online phase, CSI data from the target device is collected and converted into image, and then input into the pre-trained improved MixNet model (named MixNet-CA). The final device position is estimated using a weighted centroid algorithm based on the model's output probabilities. The proposed method is validated in an indoor environment and achieve an average positioning error of 0.362 0 m.