ultralytics 8.0.41 TF SavedModel and EdgeTPU export (#1034)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Noobtoss <96134731+Noobtoss@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>

examples/README.md

@@ -4,7 +4,7 @@ This is a list of real-world applications and walkthroughs. These can be folders
 
 | Title                                                           | Format             | Contributor                                         |
 | --------------------------------------------------------------- | ------------------ | --------------------------------------------------- |
-| [Yolov8/yolov5 ONNX Inference with C++](./Yolov8_CPP_Inference) | C++/ONNX           | [Justas Bartnykas](https://github.com/JustasBart)   |
+| [Yolov8/yolov5 ONNX Inference with C++](./YOLOv8-CPP-Inference) | C++/ONNX           | [Justas Bartnykas](https://github.com/JustasBart)   |
 | [YOLOv8-OpenCV-ONNX-Python](./YOLOv8-OpenCV-ONNX-Python)        | OpenCV/Python/ONNX | [Farid Inawan](https://github.com/frdteknikelektro) |
 
 ## How can you contribute ?

examples/YOLOv8-CPP-Inference/README.md

@@ -48,6 +48,10 @@ yolov5s.onnx:
 
 ![image](https://user-images.githubusercontent.com/40023722/217357005-07464492-d1da-42e3-98a7-fc753f87d5e6.png)
 
-This repository is based on OpenCVs dnn API to run an ONNX exported model of either yolov5/yolov8 (In theory should work for yolov6 and yolov7 but not tested). Note that for this example the networks are exported as rectangular (640x480) resolutions, but it would work for any resolution that you export as although you might want to use the letterBox approach for square images depending on your use-case.
+This repository is based on OpenCVs dnn API to run an ONNX exported model of either yolov5/yolov8 (In theory should work
+for yolov6 and yolov7 but not tested). Note that for this example the networks are exported as rectangular (640x480)
+resolutions, but it would work for any resolution that you export as although you might want to use the letterBox
+approach for square images depending on your use-case.
 
-The **main** branch version is based on using Qt as a GUI wrapper the main interest here is the **Inference** class file which shows how to transpose yolov8 models to work as yolov5 models.
+The **main** branch version is based on using Qt as a GUI wrapper the main interest here is the **Inference** class file
+which shows how to transpose yolov8 models to work as yolov5 models.

examples/tutorial.ipynb

@@ -49,7 +49,7 @@
       "source": [
         "# Setup\n",
         "\n",
-        "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) and check PyTorch and GPU."
+        "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) and check software and hardware."
       ]
     },
     {
@@ -62,7 +62,6 @@
         "outputId": "9bda69d4-e57f-404b-b6fe-117234e24677"
       },
       "source": [
-        "# Pip install method (recommended)\n",
         "%pip install ultralytics\n",
         "import ultralytics\n",
         "ultralytics.checks()"
@@ -80,17 +79,11 @@
       ]
     },
     {
-      "cell_type": "code",
-      "source": [
-        "# Git clone method (for development)\n",
-        "!git clone https://github.com/ultralytics/ultralytics\n",
-        "%pip install -qe ultralytics"
-      ],
+      "cell_type": "markdown",
+      "source": [],
       "metadata": {
-        "id": "TUFPge7f_1ms"
-      },
-      "execution_count": null,
-      "outputs": []
+        "id": "ZOwTlorPd8-D"
+      }
     },
     {
       "cell_type": "markdown",
@@ -655,6 +648,19 @@
         "Additional content below."
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Git clone install (for development)\n",
+        "!git clone https://github.com/ultralytics/ultralytics\n",
+        "%pip install -qe ultralytics"
+      ],
+      "metadata": {
+        "id": "uRKlwxSJdhd1"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "metadata": {