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chore: 🧹 removal of version constraint, using latest bbox annotator and removal of asset extra from installations 

- Removed specific version constraint for `supervision` package in installation command.
- Updated references from `sv.BoundingBoxAnnotator` to `sv.BoxAnnotator` for consistency with the latest library changes.
- Removed asset extension of package installation because it is has been included into main package

Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
This commit is contained in:
Onuralp SEZER 2025-07-11 02:07:31 +02:00
parent f90a94be2e
commit fa98266914
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8 changed files with 24 additions and 34 deletions
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2
docs/notebooks/annotate-video-with-detections.ipynb vendored
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@ -367,7 +367,7 @@
"<br>\n",
"\n",
"\n",
"In the code above we've created a`video_info` variable to pass information about the video to our `VideoSink`. The `VideoSink` is a cool little context manager that allows us to `write_frames()` to a video output file. We're also optionally using `tqdm` to display a progress bar with a % complete. We only scratched the surface of all of the customizable Annotators and additional features that Supervision and Inference have to offer. Stay tuned for more cookbooks on how to take advantage of them in your computer vision applications. Happy building! 🚀"
"In the code above we've created a`video_info` variable to pass information about the video to our `VideoSink`. The `VideoSink` is a cool little context manager that allows us to `write_frames()` to a video output file. We're also optionally using `tqdm` to display a progress bar with a % complete. We only scratched the surface of all of the customizable Annotators and additional features that Supervision and Inference have to offer. Stay tuned for more cookbooks on how to take advantage of them in your computer vision applications. Happy building! \ud83d\ude80"
]
}
],

4
docs/notebooks/download-supervision-assets.ipynb vendored
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@ -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\""
]
},
{

2
docs/notebooks/evaluating-alignment-of-text-to-image-diffusion-models.ipynb vendored
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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"
]
},
{

2
docs/notebooks/object-tracking.ipynb vendored
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@ -92,7 +92,7 @@
},
"outputs": [],
"source": [
"!pip install -q inference-gpu \"supervision[assets]\""
"!pip install -q inference-gpu \"supervision\""
]
},
{

2
docs/notebooks/occupancy_analytics.ipynb vendored
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@ -72,7 +72,7 @@
},
"outputs": [],
"source": [
"!pip install roboflow supervision==0.19.0 -q"
"!pip install roboflow supervision -q"
]
},
{

10
docs/notebooks/quickstart.ipynb vendored
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@ -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"
]
},
{

28
docs/notebooks/serialise-detections-to-csv.ipynb vendored
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8
docs/notebooks/serialise-detections-to-json.ipynb vendored
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@ -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",