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Mask R-CNN,: the ,Mask R-CNN, was used as baseline for instance segmentation. The default Detectron implementation was adopted, using the 1x learning schedule. Training started with a learning rate of 0.02, which was reduced by a factor of 10 at 60k and 80k iterations, and stopped at 90k iterations, on 8 synchronized GPUs, each holding 2 images per iteration. 512 RoIs were used per image.
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It’s called ,Mask R-CNN, , published by the Facebook AI Research (,FAIR,) team at ICCV 2017. The post assumes a basic understanding of deep learning and CNNs for object detection. For easier understanding, I’ll be using examples of code samples in PyTorch as its pretty popular these days.
The output ,mask, resolution is M × M = 28 × 28. Table below compares full ,Mask,^X ,R-CNN, method (i.e., ,Mask R-CNN, with ‘transfer+MLP’ and T implemented as ‘cls+box, 2-layer, LeakyReLU’) and the class-agnostic baseline using end-to-end training. ,Mask,^X ,R-CNN, outperforms these approaches by a large margin (over 20% relative increase in ...
Fast-forward to 2017, and ,FAIR, researchers had won the International Conference on Computer Vision Best Paper for ,Mask R-CNN,, combining the best of computer vision worlds: object detection with semantic segmentation.