Enet

Pytorch Implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation (https://arxiv.org/abs/1606.02147). It is currently the 13th Best Model for Real-Time Semantic Segmentation on Cityscapes test .

Training Notebooks

Note: Try using the Binder badge to inspect the notebooks.

• Enet with PReLU Encoder on CamVid
• Enet with Mish Encoder on CamVid

Training Results on CamVid Dataset

Inference on Training Data

Inference on Validation Data

TODO

• Implement Vanilla Enet Architecture
• Encorporate Custom Activations for Codebase
• Train Enet on CamVid
• Train Enet with Mish Encoder on CamVid
• Experiment to find best Mish Version of Enet -> PReLU encoder + Mish Decoder
• Repeat Same experiments for Cityscapes Dataset
• Repeat Same experiments for SUN RGB-D Dataset
• Implement Lovasz Softmax Loss

References

• ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
• Tensorflow Enet
• ENet on TowardsDataScience
• Mish: Self Regularized Non-Monotonic Activation Function
• Mish on Arxiv