Multi-Scale Training
SNIPER
Introduced by Singh et al. in SNIPER: Efficient Multi-Scale TrainingSNIPER is a multi-scale training approach for instance-level recognition tasks like object detection and instance-level segmentation. Instead of processing all pixels in an image pyramid, SNIPER selectively processes context regions around the ground-truth objects (a.k.a chips). This can help to speed up multi-scale training as it operates on low-resolution chips. Due to its memory-efficient design, SNIPER can benefit from Batch Normalization during training and it makes larger batch-sizes possible for instance-level recognition tasks on a single GPU.
Source: SNIPER: Efficient Multi-Scale Training
Papers
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Usage Over Time
Components
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
