Image Scaling Strategies
FixRes
Introduced by Touvron et al. in Fixing the train-test resolution discrepancyFixRes is an image scaling strategy that seeks to optimize classifier performance. It is motivated by the observation that data augmentations induce a significant discrepancy between the size of the objects seen by the classifier at train and test time: in fact, a lower train resolution improves the classification at test time! FixRes is a simple strategy to optimize the classifier performance, that employs different train and test resolutions. The calibrations are: (a) calibrating the object sizes by adjusting the crop size and (b) adjusting statistics before spatial pooling.
Source: Fixing the train-test resolution discrepancy
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 10 | 31.25% |
| Fine-Grained Image Classification | 5 | 15.63% |
| General Classification | 4 | 12.50% |
| Semantic Segmentation | 2 | 6.25% |
| Document Image Classification | 2 | 6.25% |
| Self-Supervised Learning | 1 | 3.13% |
| Document Layout Analysis | 1 | 3.13% |
| Efficient ViTs | 1 | 3.13% |
| Classification | 1 | 3.13% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
