Merge pull request #87 from jo-mueller/add-more-info-on-datasets

Added info on target dataset for upload

docs/johannes_mueller/yolo_from_omero/train_yolo.ipynb

@@ -253,6 +253,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### Train/Test/Validation split\n",
+    "\n",
     "Before we dive into the training, we have to do one final step. It is common practice in deep learning, to split the data into a training, validation and a testing cohort. This is done to evaluate the model on data it has not seen before. We will do a 70/20/10% split for training, validation and testing, respectively. We first create the folder structure:"
    ]
   },
@@ -642,7 +644,9 @@
     "\n",
     "```{note}\n",
     " The steps up to this point can take a bit of time so you may have to reconnect to the omero server.\n",
-    "```"
+    "```\n",
+    "\n",
+    "One issue/question may arise here. *Where to upload the data?* My recommendation would be to create a new dataset on the omero server and upload the data there. In the following code, the dataset I created carries the id `260`, so'd have to replace this with the id of the dataset you created. *But why not just upload it tothe dataset I was already using up until here?* Well, you could do that, but it would be a bit messy because ad hoc it would be impossible to differentiate between the annotations you made manually above and the predictions done by YOLO. To keep it clean, it's better to create a new dataset."
    ]
   },
   {
@@ -654,7 +658,8 @@
     "image_to_upload = io.imread(test_image)\n",
     "image_id = ezomero.post_image(\n",
     "    conn, image=image_to_upload[None, None, None, :], dim_order='tczyx',\n",
-    "    dataset_id=260, image_name='example_prediction'\n",
+    "    dataset_id=260,  # replace 260 with the id of the dataset you want to upload the image to\n",
+    "    image_name='example_prediction'\n",
     "    )\n",
     "\n",
     "roid_id = ezomero.post_roi(conn, image_id=image_id, shapes=rectangles)"