plot the effect of full training. (#243)

tasks/face_segmentation/aml/readme.md

@@ -44,13 +44,18 @@ validation scores and are discarded:
 ![snapshot](images/pareto.gif)
 
 When the search completes you can run [train_pareto.py](../../train_pareto.py) to fully train the
-pareto models then you can run [snp_test.py](../../snp_test.py) to compute the F1 scores for these
-fully trained models on your Qualcomm hardware, the following is a plot you can get from the
-notebook showing the final results.  Notice that the Qualcomm hardware mostly matches our earlier
-`val_iou` pareto curve, but not exactly. The dots shown in gray have fallen off the pareto frontier.
-This is why it is always good to test your models on the target hardware.  Even better if that
-testing can be done in the search loop so that the search finds models that work well on the target
-hardware, as we have done in this face segmentation example:
+pareto models. When training is finished you can visualize the results of full training in the notebook and you should see something like this:
+
+![full_training](images/full_training.png)
+
+
+Then you can run [snp_test.py](../../snp_test.py) to compute the F1 scores for these fully trained
+models on your Qualcomm hardware, the following is a plot you can get from the notebook showing the
+final results comparing F1 accuracy with inference latency.  Notice that the Qualcomm hardware F1
+score mostly matches our earlier `val_iou` pareto curve, but not exactly. The dots shown in gray
+have fallen off the pareto frontier. This is why it is always good to test your models on the target
+hardware.  Even better if that testing can be done in the search loop so that the search finds
+models that work well on the target hardware, as we have done in this face segmentation example:
 
 ![errors](images/final_results.png)