Image Models
HaloNet
Introduced by Vaswani et al. in Scaling Local Self-Attention for Parameter Efficient Visual BackbonesA HaloNet is a self-attention based model for efficient image classification. It relies on a local self-attention architecture that efficiently maps to existing hardware with haloing. The formulation breaks translational equivariance, but the authors observe that it improves throughput and accuracies over the centered local self-attention used in regular self-attention. The approach also utilises a strided self-attentive downsampling operation for multi-scale feature extraction.
Source: Scaling Local Self-Attention for Parameter Efficient Visual Backbones
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Anomaly Detection | 1 | 16.67% |
| Unsupervised Anomaly Detection | 1 | 16.67% |
| Image Classification | 1 | 16.67% |
| Instance Segmentation | 1 | 16.67% |
| Object Detection | 1 | 16.67% |
| Semantic Segmentation | 1 | 16.67% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
