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03_COSDD/bonus-exercise.ipynb

@@ -61,7 +61,7 @@
     "\n",
     "### Task 3.1.\n",
     "\n",
-    "Load the model trained in the first notebook by entering your `model_name`, or alternatively uncomment line 4 to load the pretrained model.\n",
+    "Load the model trained in the first notebook by entering your `model_name`, or alternatively, uncomment line 4 to load the pretrained model.\n",
     "</div>"
    ]
   },
@@ -78,7 +78,7 @@
     "model_name = ...  # Insert a string here\n",
     "checkpoint_path = os.path.join(\"checkpoints\", model_name)\n",
     "\n",
-    "# checkpoint_path = TODO: Add pretrained\n",
+    "# checkpoint_path = \"checkpoints/mito-confocal-pretrained\"\n",
     "\n",
     "hub = Hub.load_from_checkpoint(os.path.join(checkpoint_path, \"final_model.ckpt\")).cuda()"
    ]

03_COSDD/exercise.ipynb

@@ -487,6 +487,8 @@
     "2. Enter `conda activate 05_image_restoration` to activate an environment with Tensorboard installed.\n",
     "3. Enter `tensorboard --logdir 05_image_restoration/03_COSDD/checkpoints`\n",
     "4. Finally, open a browser and enter localhost:6006 in the address bar.\n",
+    "\n",
+    "Once you're in tensorboard, you'll see the training logs of your model and the logs of a model that's been trained for 3.5 hours.\n",
     "</div>"
    ]
   },
@@ -699,7 +701,7 @@
     "\n",
     "Our model was only trained for 10 minutes. This is long enough to get some denoising results, but a model trained for longer would do better. In the cell below, load the trained model by recalling the value you gave for `model_name`. Then procede through the notebook to look at how well it performs. \n",
     "\n",
-    "Once you reach the end of the notebook, return to this cell to load a model that has been trained for 24 hours by uncommenting line 4, then run the notebook again to see how much difference the extra training time makes.\n",
+    "Once you reach the end of the notebook, return to this cell to load a model that has been trained for 3.5 hours by uncommenting line 4, then run the notebook again to see how much difference the extra training time makes. \n",
     "</div>"
    ]
   },
@@ -712,7 +714,7 @@
     "model_name = ...   ### Insert a string here\n",
     "checkpoint_path = os.path.join(\"checkpoints\", model_name)\n",
     "\n",
-    "# checkpoint_path = TODO: Add pretrained checkpoint ### Once you reach the bottom of the notebook, return here and uncomment this line to see the pretrained model\n",
+    "# checkpoint_path = \"checkpoints/mito-confocal-pretrained\" ### Once you reach the bottom of the notebook, return here and uncomment this line to see the pretrained model\n",
     "\n",
     "hub = Hub.load_from_checkpoint(os.path.join(checkpoint_path, \"final_model.ckpt\"))\n",
     "\n",
@@ -735,7 +737,7 @@
     "model_name = \"mito-confocal\"   ### Insert a string here\n",
     "checkpoint_path = os.path.join(\"checkpoints\", model_name)\n",
     "\n",
-    "# checkpoint_path = TODO: Add pretrained checkpoint ### Once you reach the bottom of the notebook, return here and uncomment this line to see the pretrained model\n",
+    "# checkpoint_path = \"checkpoints/mito-confocal-pretrained\" ### Once you reach the bottom of the notebook, return here and uncomment this line to see the pretrained model\n",
     "\n",
     "hub = Hub.load_from_checkpoint(os.path.join(checkpoint_path, \"final_model.ckpt\"))\n",
     "\n",

04_DenoiSplit/exercise.ipynb

@@ -796,7 +796,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6178515",
+   "id": "d2801e74",
    "metadata": {},
    "source": [
     "<hr style=\"height:2px;\"><div class=\"alert alert-block alert-success\"><h1>End of the exercise</h1>\n",