SNIPER 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 TrainingPaper | Code | Results | Date | Stars |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |