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Repository of model development for GhostNetZero.Ai

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microsoft/GhostNetAI

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Example commands:

Train

  • Every existing hyperparameters are in the configuration files. After a model is trained, a ckpt file will be saved under theweights folder.
python main.py --config ./configs/ghostseg_de_plain_111824.yaml

Validation

  • Validation will not output npz file for post process. Just outputs evaluation metrics.
python main.py --config ./configs/ghostseg_de_plain_111824.yaml --evaluate ./weights/GhostSeg_DE/Plain/GhostSeg_DE_Plain_111824-0-epoch\=2514-valid_mean_IoU\=0.77.ckpt --val

Test

  • Test will save an npz file that has images, labels, logits, and all relevent data for post-processing.
python main.py --config ./configs/ghostseg_de_plain_111824.yaml --evaluate ./weights/GhostSeg_DE/Plain/GhostSeg_DE_Plain_111824-0-epoch\=2514-valid_mean_IoU\=0.77.ckpt

Prediction

  • Prediction only loads a data root and outputs polygon predictions, center lines of the polygons, and centroids of these polygons. All coordinates are in percentages of image widths and heights.
 python main.py --config ./configs/ghostseg_de_plain_111824.yaml --evaluate ./weights/GhostSeg_DE/Plain/GhostSeg_DE_Plain_111824-0-epoch\=2514-valid_mean_IoU\=0.77.ckpt --predict --predict_root ~/datasets/wwf_seg/Germany/val/

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