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Author: nelaturuharsha

examples/image_classification_tutorial.ipynb

@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -229,12 +229,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "(\"Dataset Statistics [{'AnnotationSet ID': 77, 'AnnotationSet name': 'Image \"\n \"classification', 'n_images': 140, 'n_classes': 3, 'n_objects': 0, \"\n \"'top_3_classes': [{'name': 'Hard-leaved pocket orchid', 'count': 60}, \"\n \"{'name': 'Canterbury bells', 'count': 40}, {'name': 'Pink primrose', \"\n \"'count': 40}], 'creation_date': None, 'last_modified_date': \"\n \"'2020-08-04T06:33:04.790286Z'}]\")\n"
+    }
+   ],
    "source": [
     "data_stats = flowers.get_annotation_statistics()\n",
     "pprint(\"Dataset Statistics {}\".format(data_stats))"
@@ -290,8 +296,7 @@
     "A custom PyTorch ```Dataset``` object defined below is used to load data.\n",
     "\n",
     "In order to adapt this to your dataset, the following are required:\n",
-    "\n",
-    "- **Path to data:** Path to the Data Folder\n",
+    "- **Path to Tags:** Path to Tags file for Train, Test, Validation split CSV generated by Remo\n",
     "- **Path to Annotations:** Path to Annotations CSV File (Format : file_name, class_name)\n",
     "- **Mapping:** Python dictionary containing mapping of class name and class index (Format : {\"class_name\" : \"class_index\"})\n",
     "- **transforms:** Transforms to be applied to the images before passing it to the network."
@@ -423,7 +428,7 @@
     "\n",
     "The pre-trained weights of the ```ResNet-18``` model with ImageNet are used in this tutorial.\n",
     "\n",
-    "To train the model, the following details are passed to the ```train_model()``` function\n",
+    "To train the model, the following details are to be specified.\n",
     "\n",
     "1. **Model:** The edited version of the pre-trained model.\n",
     "2. **Data Loaders:** The dictionary containing our training and validation dataloaders\n",
@@ -624,7 +629,7 @@
    "outputs": [],
    "source": [
     "classes = list(mapping.keys())\n",
-    "flowers.create_annotation_set(\"Image Classification\", name=\"model_predictions\", path_to_annotation_file=\"./results.csv\", classes=classes)"
+    "flowers.create_annotation_set(\"Image Classification\", name=\"model_predictions\", path_to_annotation_file=\"./results.csv\")"
    ]
   },
   {
@@ -667,7 +672,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.10-final"
   },
   "toc": {
    "base_numbering": 1,
@@ -934,4 +939,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}