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@@ -90,7 +90,7 @@ pipeline_tag: image-segmentation
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  </div>
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  <br>
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- <div style="border:0px; padding:25px; background-color:#FDFFF4 ; padding-top:10px; padding-bottom:1px;">
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  <h1>FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model</h1>
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  <p>The general characteristics of this specific model <strong>FLAIR-INC_RVBIE_resnet34_unet_15cl_norm</strong> are :</p>
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  <ul style="list-style-type:disc;">
@@ -224,15 +224,11 @@ 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="display: flex; justify-content: space-between;">
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- <div style="width: 45%;">
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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: 100%;"/>
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- </div>
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- <div style="width: 45%;">
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  <p>VALIDATION loss</p>
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- <img src="val_loss_FLAIR-INC_RGBIE_resnet34_unet_15cl_norm.png" alt="drawing" style="width: 100%;"/>
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- </div>
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  </div>
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@@ -287,18 +283,15 @@ 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="display: flex; justify-content: space-between;">
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- <div style="width: 45%;">
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- <p>Normalised confusion Matrix (precision)</p>
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- <img src="FLAIR-INC_RVBIE_resnet34_unet_15cl_norm_cm-precision.png" alt="drawing" style="width: 100%;"/>
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- </div>
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- <div style="width: 45%;">
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- <p>Normalised Confusion Matrix (recall)</p>
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- <img src="FLAIR-INC_RVBIE_resnet34_unet_15cl_norm_cm-recall.png" alt="drawing" style="width: 100%;"/>
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- </div>
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  </div>
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  ### Results
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  Samples of results
 
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  </div>
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  <br>
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+ <div style="border:1px solid black; padding:25px; background-color:#FDFFF4 ; padding-top:10px; padding-bottom:1px;">
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  <h1>FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model</h1>
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  <p>The general characteristics of this specific model <strong>FLAIR-INC_RVBIE_resnet34_unet_15cl_norm</strong> are :</p>
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  <ul style="list-style-type:disc;">
 
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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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+ <img src="val_loss_FLAIR-INC_RGBIE_resnet34_unet_15cl_norm.png" alt="drawing" style="width: 60%;"/>
 
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  </div>
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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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+ <img src="FLAIR-INC_RVBIE_resnet34_unet_15cl_norm_cm-recall.png" alt="drawing" style="width: 70%;"/>
 
 
 
 
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  </div>
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+
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  ### Results
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  Samples of results