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 | 12 | 26.09% |
| Autonomous Driving | 5 | 10.87% |
| Quantization | 2 | 4.35% |
| Autonomous Vehicles | 2 | 4.35% |
| Real-Time Semantic Segmentation | 2 | 4.35% |
| Instance Segmentation | 2 | 4.35% |
| Scene Understanding | 2 | 4.35% |
| Optical Character Recognition (OCR) | 1 | 2.17% |
| Multi-Task Learning | 1 | 2.17% |
Convolution
Max Pooling
