change model encoder and decoder

[masoud-khalilian]

Here are some suggestions for modifying the encoder and decoder components of the ENet, ICNet, and BiSeNet models:

ENet:

Encoder Modification: Increase the depth of the encoder by adding more convolutional layers or using larger filter sizes to capture more complex features. Experiment with different types of convolutional layers, such as dilated convolutions or depthwise separable convolutions, to enhance the model's representation capabilities. Decoder Modification: Increase the depth or complexity of the decoder by adding more upsampling layers or using skip connections to capture finer details. Consider using transposed convolutions or interpolation techniques for upsampling to increase the resolution of the feature maps. Incorporate skip connections from earlier layers of the encoder to the decoder to enable the decoder to leverage both low-level and high-level features.

ICNet:

Encoder Modification: Adjust the scales at which the different branches of the encoder operate to capture information at appropriate resolutions for your specific task. Explore different backbone architectures for the encoder, such as ResNet or MobileNet, to benefit from their respective strengths in feature extraction. Decoder Modification: Experiment with fusion mechanisms between the branches, such as element-wise summation or concatenation, to combine multi-scale features effectively. Consider adding additional upsampling or refinement layers to the decoder to enhance the resolution and details of the segmentation output.

BiSeNet:

Encoder Modification: Increase the depth or width of both the spatial path and context path of the encoder to capture more intricate spatial details and global context. Explore different backbone architectures, such as ResNet or DenseNet, for the encoder to improve feature extraction capabilities. Decoder Modification: Adjust the depth or complexity of the decoder by adding more upsampling layers or using skip connections to recover spatial resolution and refine segmentation results. Consider incorporating auxiliary decoding heads at different scales to capture multi-scale information and further improve segmentation performance. These suggestions provide a starting point for modifying the encoder and decoder components of these models. However, it's crucial to experiment and fine-tune these modifications based on your specific dataset, computational resources, and performance requirements. The best approach may vary depending on the nature of your project and the characteristics of your data.

[masoud-khalilian]

Read the following paper Context Encoders: Feature Learning by Inpainting