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README.md
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@@ -224,7 +224,7 @@ The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained on a HPC/AI resour
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FLAIR-INC_RVBIE_resnet34_unet_15cl_norm was obtained for num_epoch=76 with corresponding val_loss=0.56.
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<div style="position: relative;">
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<p>TRAIN loss</p>
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<img src="train_loss_FLAIR-INC_RGBIE_resnet34_unet_15cl_norm.png" alt="drawing" style="width: 60%;"/>
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<p>VALIDATION loss</p>
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@@ -283,7 +283,7 @@ The following illustration gives the resulting confusion matrix :
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* Right : normalised acording to rows, rows sum at 100% and the **recall** is on the diagonal of the matrix
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<div style="position: relative;">
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<p>Normalized Confusion Matrix (precision)</p>
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<img src="FLAIR-INC_RVBIE_resnet34_unet_15cl_norm_cm-precision.png" alt="drawing" style="width: 70%;"/>
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<p>Normalized Confusion Matrix (recall)</p>
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FLAIR-INC_RVBIE_resnet34_unet_15cl_norm was obtained for num_epoch=76 with corresponding val_loss=0.56.
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<div style="position: relative; text-align center;">
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<p>TRAIN loss</p>
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<img src="train_loss_FLAIR-INC_RGBIE_resnet34_unet_15cl_norm.png" alt="drawing" style="width: 60%;"/>
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<p>VALIDATION loss</p>
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* Right : normalised acording to rows, rows sum at 100% and the **recall** is on the diagonal of the matrix
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<div style="position: relative; text-align center;">
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<p>Normalized Confusion Matrix (precision)</p>
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<img src="FLAIR-INC_RVBIE_resnet34_unet_15cl_norm_cm-precision.png" alt="drawing" style="width: 70%;"/>
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<p>Normalized Confusion Matrix (recall)</p>
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