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Glenn Jocher 2 years ago
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examples/tutorial.ipynb
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@ -22,7 +22,7 @@
"source": [ "source": [
"<div align=\"center\">\n", "<div align=\"center\">\n",
"\n", "\n",
" <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n", " <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n",
" <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n", " <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n",
"\n", "\n",
"\n", "\n",
@ -32,11 +32,11 @@
" <a href=\"https://www.kaggle.com/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n", " <a href=\"https://www.kaggle.com/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
"<br>\n", "<br>\n",
"\n", "\n",
"Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 is the latest version of the YOLO (You Only Look Once) object detection and image segmentation model developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n", "Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) object detection and image segmentation model developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n",
"\n", "\n",
"The YOLOv8 models are designed to be fast, accurate, and easy to use, making them an excellent choice for a wide range of object detection and image segmentation tasks. They can be trained on large datasets and are capable of running on a variety of hardware platforms, from CPUs to GPUs.\n", "The YOLOv8 models are designed to be fast, accurate, and easy to use, making them an excellent choice for a wide range of object detection and image segmentation tasks. They can be trained on large datasets and are capable of running on a variety of hardware platforms, from CPUs to GPUs.\n",
"\n", "\n",
"Whether you are a seasoned machine learning practitioner or new to the field, we hope that the resources on this page will help you get the most out of YOLOv8. Please feel free to browse the <a href=\"https://docs.ultralytics.com/\">YOLOv8 Docs</a> and reach out to us with any questions or feedback.\n", "Whether you are a seasoned machine learning practitioner or new to the field, we hope that the resources in this notebook will help you get the most out of YOLOv8. Please feel free to browse the <a href=\"https://docs.ultralytics.com/\">YOLOv8 Docs</a> and reach out to us with any questions or feedback.\n",
"\n", "\n",
"</div>" "</div>"
] ]
@ -57,8 +57,7 @@
"metadata": { "metadata": {
"id": "wbvMlHd_QwMG", "id": "wbvMlHd_QwMG",
"colab": { "colab": {
"base_uri": "https://localhost:8080/", "base_uri": "https://localhost:8080/"
"height": 0
}, },
"outputId": "d7b7b0a6-6a29-4e6d-e404-8d67d2be86e2" "outputId": "d7b7b0a6-6a29-4e6d-e404-8d67d2be86e2"
}, },
@ -68,7 +67,7 @@
"import ultralytics\n", "import ultralytics\n",
"ultralytics.checks()" "ultralytics.checks()"
], ],
"execution_count": 1, "execution_count": null,
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
@ -101,7 +100,7 @@
"source": [ "source": [
"# 1. Predict\n", "# 1. Predict\n",
"\n", "\n",
"`yolo mode=predict` runs YOLOv8 inference on a variety of sources, downloading models automatically from the [latest YOLOv8 release](https://github.com/ultralytics/ultralytics/releases), and saving results to `runs/predict`.\n" "YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command. `yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/config/) in the YOLOv8 [Docs](https://docs.ultralytics.com).\n"
] ]
}, },
{ {
@ -114,10 +113,10 @@
"outputId": "bc3ee5db-5c36-4dcc-d016-d6b93c756eb2" "outputId": "bc3ee5db-5c36-4dcc-d016-d6b93c756eb2"
}, },
"source": [ "source": [
"!yolo task=detect mode=predict model=yolov8n.pt conf=0.25 source='https://ultralytics.com/images/zidane.jpg'\n", "# Run inference on an image with YOLOv8n\n",
"# display.Image(filename='runs/detect/predict/zidane.jpg', width=600)" "!yolo task=detect mode=predict model=yolov8n.pt conf=0.25 source='https://ultralytics.com/images/zidane.jpg'"
], ],
"execution_count": 2, "execution_count": null,
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
@ -155,7 +154,7 @@
}, },
"source": [ "source": [
"# 2. Val\n", "# 2. Val\n",
"Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv8 release](https://github.com/ultralytics/ultralytics/releases). To show results by class use the `--verbose` flag." "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. The latest YOLOv8 [models](https://github.com/ultralytics/ultralytics/releases) are downloaded automatically the first time they are used."
] ]
}, },
{ {
@ -185,7 +184,7 @@
"# Validate YOLOv8n on COCO128 val\n", "# Validate YOLOv8n on COCO128 val\n",
"!yolo task=detect mode=val model=yolov8n.pt data=coco128.yaml" "!yolo task=detect mode=val model=yolov8n.pt data=coco128.yaml"
], ],
"execution_count": 3, "execution_count": null,
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
@ -307,7 +306,7 @@
"# Train YOLOv8n on COCO128 for 3 epochs\n", "# Train YOLOv8n on COCO128 for 3 epochs\n",
"!yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml epochs=3 imgsz=640" "!yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml epochs=3 imgsz=640"
], ],
"execution_count": 4, "execution_count": null,
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
@ -460,7 +459,7 @@
"source": [ "source": [
"# 4. Export\n", "# 4. Export\n",
"\n", "\n",
"Export a YOLOv8 model to any format.\n", "Export a YOLOv8 model to any supported format with the `format` argument, i.e. `format=onnx`.\n",
"\n", "\n",
"Format | `format=...**bold text**` | Model\n", "Format | `format=...**bold text**` | Model\n",
"--- | --- | ---\n", "--- | --- | ---\n",
@ -493,7 +492,7 @@
"id": "CYIjW4igCjqD", "id": "CYIjW4igCjqD",
"outputId": "f0910315-d678-45b4-c283-3ccd018a5024" "outputId": "f0910315-d678-45b4-c283-3ccd018a5024"
}, },
"execution_count": 5, "execution_count": null,
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
@ -522,7 +521,7 @@
"source": [ "source": [
"# 5. Python Usage\n", "# 5. Python Usage\n",
"\n", "\n",
"YOLOv8 was reimagined using python-first principles for the most seamless python YOLO experience ever." "YOLOv8 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLOv8 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See a detailed Python usage examples in the YOLOv8 [Docs](https://docs.ultralytics.com)."
], ],
"metadata": { "metadata": {
"id": "kUMOQ0OeDBJG" "id": "kUMOQ0OeDBJG"

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