Image Model Blocks
ENet Initial Block
Introduced by Paszke et al. in ENet: A Deep Neural Network Architecture for Real-Time Semantic SegmentationThe ENet Initial Block is an image model block used in the ENet semantic segmentation architecture. Max Pooling is performed with non-overlapping 2 × 2 windows, and the convolution has 13 filters, which sums up to 16 feature maps after concatenation. This is heavily inspired by Inception Modules.
Source: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semantic Segmentation | 13 | 23.21% |
| Autonomous Driving | 5 | 8.93% |
| Image Segmentation | 2 | 3.57% |
| Quantization | 2 | 3.57% |
| Autonomous Vehicles | 2 | 3.57% |
| Real-Time Semantic Segmentation | 2 | 3.57% |
| Deep Learning | 2 | 3.57% |
| regression | 2 | 3.57% |
| Medical Image Analysis | 2 | 3.57% |
Convolution
Max Pooling
