Merge pull request #1399 from jyaistMap/next

Syncing the next branch with changes made to master within last month

Author: jyaistMap

guide/14-deep-learning/how_AutoDL_works.ipynb

@@ -66,23 +66,22 @@
     "- RetinaNet\n",
     "- FasterRCNN\n",
     "- YOLOv3\n",
-    "- MMDetection:\n",
-    "    - atss\n",
-    "    - carafe\n",
-    "    - cascade_rcnn\n",
-    "    - cascade_rpn\n",
-    "    - dcn\n",
+    "- ATSS\n",
+    "- CARAFE\n",
+    "- CascadeRCNN\n",
+    "- CascadeRPN\n",
+    "- DCN\n",
     "\n",
     "**Pixel Classification**\n",
     "\n",
     "- DeepLab\n",
     "- UnetClassifier\n",
     "- PSPNetClassifier\n",
-    "    - ann\n",
-    "    - apcnet\n",
-    "    - ccnet\n",
-    "    - cgnet\n",
-    "    - deeplabv3\n"
+    "- ANN\n",
+    "- APCNet\n",
+    "- CCNet\n",
+    "- CGNet\n",
+    "- HRNet\n"
    ]
   },
   {
@@ -166,13 +165,13 @@
     "    - _advanced_ : To be used when the user also wants to tune hyper parameters of the two best performing models from the basic mode.\n",
     "    \n",
     "\n",
-    "- `networks` (Optional Parameter):\n",
-    "    The list of models that will be used in the training process. If the user does not provide the parameter value, the `AutoDL` class selects all of the supported networks, however the user can select one or more networks by passing the network names in a list.\n",
+    "- `network` (Optional Parameter):\n",
+    "    The list of models that will be used in the training process. If the user does not provide the parameter value, the `AutoDL` class selects all of the supported networks, however the user can select one or more networks by passing the network names as string in a list.\n",
     "\n",
     "    - _Supported Object Detection models_\n",
-    "        - SingleShotDetector, RetinaNet, FasterRCNN, YOLOv3, MMDetection\n",
+    "        - SingleShotDetector, RetinaNet, FasterRCNN, YOLOv3, ATSS, CARAFE, CascadeRCNN, CascadeRPN, DCN\n",
     "    - _Supported Object Detection models_\n",
-    "        - DeepLab, UnetClassifier, PSPNetClassifier, MMSegmentation\n",
+    "        - DeepLab, UnetClassifier, PSPNetClassifier, ANN, APCNet, CCNet, CGNet, HRNet\n",
     "        \n",
     "\n",
     "- `verbose` (Optional Parameter): Optional Boolean.\n",
@@ -539,7 +538,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -553,7 +552,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.11"
+   "version": "3.9.15"
   }
  },
  "nbformat": 4,

samples/04_gis_analysts_data_scientists/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning.ipynb

@@ -96,7 +96,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can download vehicle training data from [here](https://github.com/Esri/arcgis-python-api/tree/master/samples/04_gis_analysts_data_scientists/data/vehicle_training_data.zip). Extract the downloaded file to get your training data."
+    "You can download vehicle training data from [here](https://www.arcgis.com/home/item.html?id=ccaa060897e24b379a4ed2cfd263c15f). Extract the downloaded file to get your training data."
    ]
   },
   {