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Instance Segmentation

Sample inference script for torchscript exported instance segmentation model Mask RCNN code example import torch import numpy as np from PIL import …

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Instance Segmentor

Instance segmentors combine the advantages of semantic segmentors and object detectors . On the one hand, they are able to predict pixel-perfect …

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IoU (Intersection over Union)

IoU is used to estimate how well a predicted mask or bounding box matches the ground truth data. IoU also known as Jaccard index or Jaccard …

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Iterations are the number of steps through batches of the training data needed to complete one epoch. For better understanding, consider the …

Development User documentation

Label class prediction

Widgets GPU Consumption Running time Inference time Accuracy VS Number of Iterations Loss VS Number of Iterations Predictions on classes Confusion …

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The majority of the segmentation architectures like Pyramid Architecture Network , PSPnet, UNet have used the encoding-decoding strategy. The encoder …

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mAP (mean Average Precision)

Mean average precision (mAP) is the most meaningful metric for object detectors , instance , and semantic segmentors . It incorporates the trade-off …

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Mask R-CNN

Mask R-CNN is the most used architecture for instance segmentation . It is almost built the same way as Faster R-CNN . The major difference is that …

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Min delta

Intuition As a neural network accumulates more parameters, it is more exposed to overfitting. Too many epochs can lead to overfitting of the training …

Development User documentation

Model Dashboard

Model dashboard user interface overview Dashboard overview in detail format Dashboard in widget format Dashboard Information: Split name Experiment …

Development Deploy User documentation

Model Exports

Exporting models for deployment to personal or edge devices In Model Playground, you have the option of exporting the best model from an experiment. …

Development Deploy MP Wiki

Momentum (SGD)

Momentum speeds up the SGD optimizer to reach the local minimum quicker. If we move in the same direction in the loss landscape, the optimizer will …

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