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Merge pull request #1880 from roboflow/docs/cookbook-updates

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chore: 🧹 removal of version constraint, using latest bbox annotator and removal of asset extra from installations
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Piotr Skalski authored and GitHub committed 2025-07-11 14:10:32 +02:00
2 parents 20066174e5 bf2a67b85b
commit 8c581e4855
10 files changed
+58 -59
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docs/notebooks/annotate-video-with-detections.ipynb
Vendored
+2 -3
View File
@@ -93,7 +93,7 @@
},
"outputs": [],
"source": [
"!pip install -q inference-gpu \"supervision[assets]\""
"!pip install -q inference-gpu \"supervision\""
]
},
{
@@ -156,7 +156,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -207,7 +207,6 @@
],
"source": [
"import supervision as sv\n",
"from supervision.assets import download_assets, VideoAssets\n",
"from inference.models.utils import get_roboflow_model\n",
"\n",
"\n",
docs/notebooks/count-objects-crossing-the-line.ipynb
Vendored
+24 -14
View File
@@ -130,7 +130,7 @@
},
"outputs": [],
"source": [
"!pip install -q \"ultralytics<=8.3.40\" \"supervision==0.18.0\""
"!pip install -q ultralytics supervision"
]
},
{
@@ -329,7 +329,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -342,19 +342,19 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x.pt to 'yolov8x.pt'...\n"
"Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt to 'yolo11x.pt'...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 131M/131M [00:00<00:00, 241MB/s]\n"
"100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 109M/109M [00:10<00:00, 11.5MB/s] \n"
]
}
],
"source": [
"model = YOLO(\"yolov8x.pt\")\n",
"model = YOLO(\"yolo11x.pt\")\n",
"\n",
"results = model(frame, verbose=False)[0]\n",
"detections = sv.Detections.from_ultralytics(results)"
@@ -384,7 +384,7 @@
"outputs": [
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAA7YAAAIfCAYAAABEnkAKAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs/c2OJEuyJoh9ombuEZl5qupW9+2+zR6CIDDAEOBmZsHH4GPwLbjgS/A1+AoEAYLccMVZEwQH5ACc7nur6vxkZri7qQoXKj+fqHlm5SEXxAXCCnUywsPcTFVUVOSTXxVVVbxf79f79X69X+/X+/V+vV/v1/v1fr1f79e/0qv9/3sA79f79X69X+/X+/V+vV/v1/v1fr1f79f79f/L9W7Yvl/v1/v1fr1f79f79X69X+/X+/V+vV//qq93w/b9er/er/fr/Xq/3q/36/16v96v9+v9+ld9vRu279f79X69X+/X+/V+vV/v1/v1fr1f79e/6uvdsH2/3q/36/16v96v9+v9er/er/fr/Xq//lVf74bt+/V+vV/v1/v1fr1f79f79X69X+/X+/Wv+no3bN+v9+v9er/er/fr/Xq/3q/36/16v96vf9XXu2H7fr1f79f79X69X+/X+/V+vV/v1/v1fv2rvt4N2/fr/Xq/3q/36/16v96v9+v9er/er/frX/W1/+iN/5v/7f8OAkClfi5tgwBoEKgqRARNBCKAqt3UBAOAyPyyiMbPgEBE4l4RgWDMz/x3+9n/zoOIZwIQhT1r3j0Ecxz+PlWINntWPHF+GUDbNmDYvc2eszUIBM0+92fn+HOMgGIMRWsCFfXZEbEq7Ro2qCoUAmkNqp2IhjneJ9+zl871gGKoQuxNIg2qA5ABaLMv65yt+GPtuQq01gCnrwy0Pp8xoLGe/l3BpKEORZPpExlQSBMo/PNJizHG/E5rc05OPyiG8YsAGE2AYeOyH0Q2ovGAjLmOaHMeqoBgg6pAMdBANEOzteXPaN7SKm8u963rpZN82BRQEQyh7xndlXnJqC0ikDlRqOZn5V32NX9Hvl2N4Jvd2Y2RYyWDn8v8fAyikwpKfKo6vxtfafYc3w2atCt8InNvl3naWjeJucH2fL0k7i9kllbvGLSlC1GY9eeaqro8GPacOYa2zEIUGI3oUpconypAGzD5NHkWo5UxMoVlMp+t0FyvDrW9lzJQjP7BjRqrM/eH1DFMWuuUYbB1F6e50Pz9+ZJ0ZVYQ4Y0ekxYfQzMO0263n4WLy4Z4tz3T5zTXaM4cU3rRZhGf5JRDtPASbDC/P+y5bRBPm7wedmdTYBuIv48UF7GPReWpbmLOCdrHXmz+ZaOHlvUbRDvf5TlvLTtVff21lTcK/awgesLJ6nrvCY1EIDwhMfmoNFFjgUk3jXHMfZ/LL+u7i5SkNStDYf2oUB2pa4k3xFnO7h0j97Coxr4cvD4q6xtoaMRLxmtKMil+dHxAdGt2/4g3tlgpl7HfYBEg5jH3mKpOGVD2h8sm05+ouCV5Yb6s2d/G8h7eg8FzoD1NGizVTcVBc+/RsEQKH1WMlPpQg3eK0ICazApRTfqeV2ay38jXFhWklZcggHaIc4HK82HSWHzvkdqKz+M7ylhsmG4zHBYY0Ofp9JSFoDReRRm7qK/k3EjaR2CHiVcB1RHjSDq3opKbAtIM5+hAR5+vbwIdVWdoyNkcWt2SRYH6qsUdegLmKN9yzFf2zPoGInrItwXv6oI5CjVJLPljXca4TpzXCJI7P+R7KmuqEIZVVDmsmvwXe23YG5uJEdNdw+iEnnNh3VUYI/XtM4lRWEuSxoW1af0WSBP3NmmBk5+tMdtFjv+gzved9L6Gvh7xDMIvji1iDREDV5INMQdfi2WpVcT2kZgMIMnmwNzWxOfaFLYiOcPB7xtz7Js9bRCyUk3cOGR+83/9v/pfntbj2fXDhq2rQCgDYolN0IqG0yB4s8+GTiAqxmi+Ef13kdzkI2S/L4SznyupeV9rNhbVeAaITRkSGgJ3uDTnE4xmBpUOM9KGPa+hDcTvU7kTPViA+kKJCUNFGH9wIRKAYNKyGR2GDIgOQEcaoA7AYkb2Vk1ANOkkJIjdaNMJioSescq9MMaRQEyFFEoCvtaa/a7YIBii0DbQBNgV6MbJDhgbsa+/W5sGGGrEuC02DNBUQgDnkBvEgMbAVBRzyVJApW6aQk/SUiQFqMR7IKU0AYiaESGFL4FuvDGw7HIkuJpCjBRcERoxi8RFvsT+lQD2+QTl5+lGP5sRqTDDwXFg0QbT4eDAS8gojSWffx+DAGuA/KL95tyNhs6gQgonDK2CVAsOLPwXxgWB/B+7bP8PhbY0LRgsJfjKz5pMR9P8tcJMw2hTDaiEM0gUxfiMq0nZGw6mgk6S8gjuXNMKzPy5qZTdMVDpKEMhMiovFN5K2gUNCCScPA1huKHIy/yzgyzbZ2NR97b8bSgEHYPsj/V9DMFdyfOaQGxPwC2SfIaoYOP94jIpgL2W94gNjJVy4lgDlmQo28oE6UPpmpwRTEcWZDpkc8O2MEIUZKC4PAXJGudue183Q6H5FlJz6rFQCCNTsVrp0+HRjHcSjKjpGzc+HZyHLmD953ov1iMJTJo7gXOo+LNTLkCpr4LLhxifQAUYKqfEMDeMaHbkNMsXiI9H0hHr/NZcfknylkNakSo/lf6jhdXqnHJvaDyjAvvcbAWY29W0vCwca74I7JNfDYQAgEqYyNnA1rfwva8hGSSu92zEPlTCKMQ3JjeFzSMliO9rL5jO5dzkWETPHGdDopXg8Yn58v3Jn+UaCmkwZ5LQOq0GB8JJjsUXI2xAaK6zIPVl3Mx0MMzlWGCO3R2c1daY/KHB7xF0CX3g7OESxnGe0c33gT9XHIss09QnPxbd6mMA6bMnyopkov+1rfu4yMxVgX/jvuVaDbcV38gYUxpK3kAwet5DTsVCfB1IkxyxRnAnr7YIdMz5TQzne2CYSz7kiTTau8lrlSy5nk/EXv5svED+zuI0cPmkynIxn9N1hB0wqrlHppQ7LS3Y589TIbndw04QwxfrUrFjQvUs+7CseuB8+ovSvmGjN/3ZVddC3aFJmIZp6n55ETwMy7rIrNQAcdWPXb/LsGVhuRLCmb61ljoaGalt0k5DLd8V9/6KRdWkclEIJynfc6AnQs8R84i19V7E4hegTcIpwDMqk8772kmQlCiu/z4AaXtGYaQKBY9IrhvWn1Gj1yTTLIIJSTCnzlVQSBvE0Cl03Tgd4XWfNNYxku6kUCRkUG4G+4HUZnqmHW20NapI35cnTOm04khiO3nJGVQodJjwYmPNjN8AHD9wpVGcm1ZI8Ma/BN5oJ37zuQyE0pumsR448ZWGUcpOC2FjHcV6+MYeJ26KMab2F7gi1VAy6a18LuhaADiidKyFnNY5h8JcuziilZDP+sonc2Kg53s4DDMaQ9mv5Jl0e1+Dn3m0Z26JoOYTrSYQ6BgZKTwNPQ0vVc9kSMUWb14MBhGJfRjzUDEv5XNxXtb66fzN0OHMGEJHqfrz+yyjdIynbxYQoi7vkwRoDuSfqSMnAYHEArYVuUYEPPzdbECWNVhZcHnxlkI0cW2IgFy3ystuMLeYjxhY4sc3igjkN824FeeZb8gMkicS+/WJ/BuBUgvNQjepgyBZeDG/w9gqwF0dCP2c83AZkJEO14UpjvieCv7Y4I8ZMfxdxYWRwMBoyEGn/hyRrhswxI6c/6YEvp+vQt4aGOPkqiR5zHxsv/q6iSTN+FrHY45czwxg9pw/DBbfdg+PqdFiUpYNv8S1gvg7U9dhXY5ChBBCeUNdMkaDuUbIyBFOOlbDxGBQLOLjqY6gGKe941tRJDzTP0UGkotDNedkg47sg5V+ClLChC8pSn+mmRZe8chuF6If0absifKj1n3NkCT9WOvXgho512c3nBXvs2hs0U3rGzhS+AQv6/JaX4sfAmc8UTjrEZag9SrOYp59vO5bL3TD8dv69emY6NmUfhT30G5ZX/dkBDzVKq9c/0dWzCJThB3UJq84CJa6k511+d0yKdrrp/FbxkGOZSUG4036zPEs4d2QQ9ATPnyKvWO8PqZnzs9vXz9u2EqH6h5RnfBD2cwGAbNhxilzWaTwEV6MDRWElQTOS1pxwjMXGjFj20hqnuDpGdMtbxZTihGVsXFJRF8kwbJFYdcoC0QssGApuLy5bZOFt8HTxr5lzBmzDQx4yl71iuf8hwHApIWl+Lm3VVukH+u0Equg4rQd1YmPLMqrrlTgUd/pKXF/gA6BiCmHbYLDMYZFiWHPS9AubmAanaUJhg40bRA0dIuOlnUXQPpI7+cCsmf64bDoriuVlp58kgmcPjiBlxaHhd0Va5HRl6RXAFKnTKTEmtIW4+UShTgriqJQURVErHWsTTocpgCwLcxR6WRDA/XO/34vqoov+NfXWHL+lB4dqXdlRimUzmjGVsl4KO+XhRYVhPv4cx45b5Aui/uGf7DlO4sAdCHOgMMdScszR3WQQARNFEMpqyLWABQlkni3R9yKcSCVJEKA3MGaNqnk48gRW9++LiqTwxWxt6UoqnQkDZMFgaSIx3yNOKQtTBSmt39P8hMGhB7xntOyNHDVLHdg8BJbLkITJ76o64+QeSoj7Tc0Dm9Y5k4CN1bRERWxdw+PAMmMCw/fb7ZgGSTOlMNVb+pwQ3oUoyJAkTFA+5Yxo+TjTid7pP+ujlpYqvGqNTTWNp24KX8ouktGUnX71PIdvweu61KDBkUEVk4TzhVPJ+X0vfmgLhPENB987Cmhd9F7C3izqKtnocxPFjeMf9PLP1IOeLbSXNCtvoTWa0bKpQC408UozdNaycAYGIFVQjK64wzsPCM758njU4QJGQaaulaSZpyiCfX0Y8m0QAd9Os7QlPeW5J5Jjp6adBhtVATN6BmiV3LPpeYMKWbPYtqpZQ9opk8HXUzXkMEuSNHR6NOafs4R/laBfQyTMLine truncated
"text/plain": [
"<Figure size 1200x1200 with 1 Axes>"
]
@@ -394,7 +394,7 @@
}
],
"source": [
"bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=4)\n",
"bounding_box_annotator = sv.BoxAnnotator(thickness=6)\n",
"annotated_frame = bounding_box_annotator.annotate(frame.copy(), detections)\n",
"sv.plot_image(annotated_frame, (12, 12))"
]
@@ -419,7 +419,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -431,7 +431,7 @@
"outputs": [
{
"data": {
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"image/png": "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 truncated
"text/plain": [
"<Figure size 1200x1200 with 1 Axes>"
]
@@ -447,11 +447,11 @@
" in zip(detections.class_id, detections.confidence)\n",
"]\n",
"\n",
"bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=4)\n",
"box_annotator = sv.BoxAnnotator(thickness=6)\n",
"label_annotator = sv.LabelAnnotator(text_thickness=4, text_scale=2)\n",
"\n",
"annotated_frame = frame.copy()\n",
"annotated_frame = bounding_box_annotator.annotate(annotated_frame, detections)\n",
"annotated_frame = box_annotator.annotate(annotated_frame, detections)\n",
"annotated_frame = label_annotator.annotate(annotated_frame, detections, labels)\n",
"sv.plot_image(annotated_frame, (12, 12))"
]
@@ -587,7 +587,7 @@
},
"outputs": [],
"source": [
"bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=4)\n",
"bounding_box_annotator = sv.BoxAnnotator(thickness=4)\n",
"label_annotator = sv.LabelAnnotator(text_thickness=4, text_scale=2)\n",
"trace_annotator = sv.TraceAnnotator(thickness=4)"
]
@@ -696,11 +696,21 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"display_name": "supervision",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python"
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
}
},
"nbformat": 4,
docs/notebooks/download-supervision-assets.ipynb
Vendored
+2 -2
View File
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {
"vscode": {
"languageId": "shellscript"
@@ -25,7 +25,7 @@
},
"outputs": [],
"source": [
"!pip install -q \"supervision[assets]\""
"pip install -q \"supervision\""
]
},
{
docs/notebooks/evaluating-alignment-of-text-to-image-diffusion-models.ipynb
Vendored
+1 -1
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@@ -101,7 +101,7 @@
},
"outputs": [],
"source": [
"!pip install -q torch diffusers accelerate inference-gpu[yolo-world] dill git+https://github.com/openai/CLIP.git supervision==0.19.0rc5"
"!pip install -q torch diffusers accelerate inference-gpu[yolo-world] dill git+https://github.com/openai/CLIP.git supervision"
]
},
{
docs/notebooks/object-tracking.ipynb
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+1 -1
View File
@@ -92,7 +92,7 @@
},
"outputs": [],
"source": [
"!pip install -q inference-gpu \"supervision[assets]\""
"!pip install -q inference-gpu \"supervision\""
]
},
{
docs/notebooks/occupancy_analytics.ipynb
Vendored
+1 -1
View File
@@ -72,7 +72,7 @@
},
"outputs": [],
"source": [
"!pip install roboflow supervision==0.19.0 -q"
"!pip install roboflow supervision -q"
]
},
{
docs/notebooks/quickstart.ipynb
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+5 -5
View File
@@ -300,7 +300,7 @@
},
"outputs": [],
"source": [
"!pip install -q \"ultralytics<=8.3.40\""
"!pip install -q ultralytics"
]
},
{
@@ -495,7 +495,7 @@
"source": [
"from ultralytics import YOLO\n",
"\n",
"model = YOLO(\"yolov8x.pt\")\n",
"model = YOLO(\"yolo11x.pt\")\n",
"result = model(image, verbose=False)[0]\n",
"detections = sv.Detections.from_ultralytics(result)"
]
@@ -594,7 +594,7 @@
},
{
"cell_type": "code",
"execution_count": 33,
"execution_count": null,
"metadata": {
"id": "yM6dmicTRGl6"
},
@@ -602,7 +602,7 @@
"source": [
"from ultralytics import YOLO\n",
"\n",
"model = YOLO(\"yolov8x-seg.pt\")\n",
"model = YOLO(\"yolo11x-seg.pt\")\n",
"result = model(image, verbose=False)[0]\n",
"detections = sv.Detections.from_ultralytics(result)"
]
@@ -926,7 +926,7 @@
},
"outputs": [],
"source": [
"!pip install -q supervision[assets]"
"!pip install -q supervision"
]
},
{
docs/notebooks/serialise-detections-to-csv.ipynb
Vendored
+9 -19
View File
@@ -32,7 +32,7 @@
},
"outputs": [],
"source": [
"!pip install -q inference requests tqdm supervision==0.21.0"
"!pip install -q inference requests tqdm supervision"
]
},
{
@@ -658,14 +658,14 @@
"id": "oIK2K9zM1Kn9"
},
"source": [
"###\u00a0Annotate Image with Detections\n",
"### Annotate Image with Detections\n",
"\n",
"Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) classes."
"Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) classes."
]
},
{
"cell_type": "code",
"execution_count": 41,
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -674,20 +674,9 @@
"id": "WBv8apPz1IiP",
"outputId": "ad32b078-12d5-4457-e16d-c7a428ab858a"
},
"outputs": [
{
"data": {
"image/png": "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 truncated
"text/plain": [
"<Figure size 1200x1200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"bounding_box_annotator = sv.BoundingBoxAnnotator()\n",
"bounding_box_annotator = sv.BoxAnnotator()\n",
"label_annotator = sv.LabelAnnotator()\n",
"\n",
"annotated_frame = frame.copy()\n",
@@ -724,7 +713,8 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"display_name": "supervision",
"language": "python",
"name": "python3"
},
"language_info": {
@@ -737,7 +727,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
"version": "3.11.13"
}
},
"nbformat": 4,
docs/notebooks/serialise-detections-to-json.ipynb
Vendored
+4 -4
View File
@@ -32,7 +32,7 @@
},
"outputs": [],
"source": [
"!pip install -q inference requests tqdm supervision==0.21.0"
"!pip install -q inference requests tqdm supervision"
]
},
{
@@ -695,12 +695,12 @@
"source": [
"###\u00a0Annotate Image with Detections\n",
"\n",
"Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) classes."
"Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) classes."
]
},
{
"cell_type": "code",
"execution_count": 49,
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -722,7 +722,7 @@
}
],
"source": [
"bounding_box_annotator = sv.BoundingBoxAnnotator()\n",
"bounding_box_annotator = sv.BoxAnnotator()\n",
"label_annotator = sv.LabelAnnotator()\n",
"\n",
"annotated_frame = frame.copy()\n",
docs/theme/cookbooks.html
Vendored
+9 -9
View File
@@ -12,15 +12,15 @@
</div>
<div class="custom-grid">
<a href="/develop/notebooks/quickstart"> <p class="card repo-card" data-name="Supervision Quickstart" data-labels="ANNOTATOR,DETECTION,SAM"
data-version="v0.18.0" data-author="SkalskiP,onuralpszr"></p>
data-version="v0.26.0" data-author="SkalskiP,onuralpszr"></p>
</a>
<a href="/develop/notebooks/count-objects-crossing-the-line">
<p class="card repo-card" data-name="Count Objects Crossing the Line"
data-labels="ANNOTATORS,LINE ZONE,TRACKING" data-version="v0.18.0" data-author="SkalskiP"></p>
data-labels="ANNOTATORS,LINE ZONE,TRACKING" data-version="v0.26.0" data-author="SkalskiP"></p>
</a>
<a href="/develop/notebooks/zero-shot-object-detection-with-yolo-world">
<p class="card repo-card" data-name="Zero-Shot Object Detection with YOLO-World"
data-labels="ANNOTATORS,DETECTION,INFERENCE" data-version="v0.19.0" data-author="SkalskiP"></p>
data-labels="ANNOTATORS,DETECTION,INFERENCE" data-version="v0.26.0" data-author="SkalskiP"></p>
</a>
<a href="/develop/notebooks/download-supervision-assets">
<p class="card repo-card" data-name="Downloading Supervision Assets" data-labels="ASSETS" data-version="v0.18.0"
@@ -28,7 +28,7 @@
</a>
<a href="/develop/notebooks/annotate-video-with-detections">
<p class="card repo-card" data-name="Annotate Video with Detections" data-labels="INFERENCE,YOLOV8"
data-version="v0.18.0" data-author="nickherrig"></p>
data-version="v0.26.0" data-author="nickherrig"></p>
</a>
<a href="/develop/notebooks/object-tracking">
<p class="card repo-card" data-name="Object Tracking" data-labels="TRACKING, ANNOTATOR" data-version="v0.18.0"
@@ -36,23 +36,23 @@
</a>
<a href="/develop/notebooks/occupancy_analytics">
<p class="card repo-card" data-name="Analyzing Zone Occupancy" data-labels="ANNOTATOR,DETECTION,ZONES"
data-version="v0.19.0" data-author="stellasphere"></p>
data-version="v0.26.0" data-author="stellasphere"></p>
</a>
<a href="/develop/notebooks/evaluating-alignment-of-text-to-image-diffusion-models">
<p class="card repo-card" data-name="Evaluating Alignment of Text-to-image Diffusion Models"
data-labels="ANNOTATORS,YOLO WORLD" data-version="v0.19.0rc5" data-author="iamhatesz"></p>
data-labels="ANNOTATORS,YOLO WORLD" data-version="v0.26.0" data-author="iamhatesz"></p>
</a>
<a href="/develop/notebooks/serialise-detections-to-csv">
<p class="card repo-card" data-name="Serialise Detections to a CSV File"
data-labels="DETECTIONS,CSV SINK,INFERENCE" data-version="v0.21.0" data-author="onuralpszr"></p>
data-labels="DETECTIONS,CSV SINK,INFERENCE" data-version="v0.26.0" data-author="onuralpszr"></p>
</a>
<a href="/develop/notebooks/serialise-detections-to-json">
<p class="card repo-card" data-name="Serialise Detections to a JSON File"
data-labels="DETECTIONS,JSON SINK,INFERENCE" data-version="v0.21.0" data-author="onuralpszr"></p>
data-labels="DETECTIONS,JSON SINK,INFERENCE" data-version="v0.26.0" data-author="onuralpszr"></p>
</a>
<a href="/develop/notebooks/small-object-detection-with-sahi">
<p class="card repo-card" data-name="Small Object Detection with SAHI"
data-labels="DETECTIONS,SAHI,SMALL,OBJECT,INFERENCE" data-version="v0.23.0" data-author="ediardo"></p>
data-labels="DETECTIONS,SAHI,SMALL,OBJECT,INFERENCE" data-version="v0.26.0" data-author="ediardo"></p>
</a>
</div>
</div>