feat(docs): add GEO infrastructure for AI search visibility (#2224)
* feat: add GEO infrastructure for AI search visibility
* fix: guard GEO blocks with {% if page %} to fix 404 build error
* feat: GEO platform optimization — FAQPage, IndexNow, answer paragraphs
* fix: move Disallow:/0.*/ to User-agent:* in robots.txt
* fix: block all numeric versioned paths in robots.txt
---------
Co-authored-by: Claude Code <noreply@anthropic.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Jirka Borovec
GitHub
parent
6069c477b0
commit
c4a3038c57
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@ -56,5 +56,56 @@ jobs:
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env:
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MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.GITHUB_TOKEN }}
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run: |
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latest_tag=$(git describe --tags `git rev-list --tags --max-count=1`)
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latest_tag=$(git tag --sort=-v:refname | grep -E '^[0-9]+\.[0-9]+\.[0-9]+$' | head -1)
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mike deploy --push --update-aliases $latest_tag latest
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# IndexNow key: 0d5d9799b1cc4a39825146388c6781eb
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# This key must stay in sync across three files:
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# docs/0d5d9799b1cc4a39825146388c6781eb.txt (key file served at site root)
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# docs/theme/main.html (indexnow-key meta tag)
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# this workflow (inject step + notify step below)
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# Bing/Yandex fetch https://supervision.roboflow.com/<key>.txt to verify ownership.
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# Do NOT rename or delete the .txt file or change the key string without updating all three.
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- name: 🌐 Inject GEO root files into gh-pages
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if: >
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(github.event_name == 'push' && github.ref == 'refs/heads/develop') ||
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github.event_name == 'workflow_dispatch' ||
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(github.event_name == 'release' && github.event.action == 'published')
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run: |
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cp docs/robots.txt /tmp/robots.txt
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cp docs/llms.txt /tmp/llms.txt
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cp docs/0d5d9799b1cc4a39825146388c6781eb.txt /tmp/indexnow.txt
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git fetch origin gh-pages
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git checkout gh-pages
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cp /tmp/robots.txt robots.txt
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cp /tmp/llms.txt llms.txt
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cp /tmp/indexnow.txt 0d5d9799b1cc4a39825146388c6781eb.txt
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git add robots.txt llms.txt 0d5d9799b1cc4a39825146388c6781eb.txt
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git diff --cached --quiet || git commit -m "chore: update GEO root files (robots.txt, llms.txt, indexnow)"
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git push origin gh-pages
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- name: 📡 Notify IndexNow
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if: >
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(github.event_name == 'push' && github.ref == 'refs/heads/develop') ||
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github.event_name == 'workflow_dispatch' ||
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(github.event_name == 'release' && github.event.action == 'published')
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run: |
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curl -s -o /dev/null -w "%{http_code}" -X POST "https://api.indexnow.org/IndexNow" \
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-H "Content-Type: application/json; charset=utf-8" \
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-d '{
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"host": "supervision.roboflow.com",
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"key": "0d5d9799b1cc4a39825146388c6781eb",
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"keyLocation": "https://supervision.roboflow.com/0d5d9799b1cc4a39825146388c6781eb.txt",
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"urlList": [
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"https://supervision.roboflow.com/",
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"https://supervision.roboflow.com/latest/",
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"https://supervision.roboflow.com/latest/how_to/detect_and_annotate/",
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"https://supervision.roboflow.com/latest/how_to/track_objects/",
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"https://supervision.roboflow.com/latest/how_to/detect_small_objects/",
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"https://supervision.roboflow.com/latest/how_to/filter_detections/",
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"https://supervision.roboflow.com/latest/how_to/save_detections/",
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"https://supervision.roboflow.com/latest/how_to/count_in_zone/",
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"https://supervision.roboflow.com/latest/how_to/benchmark_a_model/",
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"https://supervision.roboflow.com/latest/how_to/process_datasets/"
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]
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}' || true
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@ -0,0 +1 @@
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0d5d9799b1cc4a39825146388c6781eb
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@ -1,3 +1,7 @@
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---
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description: "Full version history of the supervision Python library — release notes, breaking changes, new features, and deprecations for every version."
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---
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# Changelog
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### 0.28.0 <small>Unreleased</small>
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@ -1,5 +1,6 @@
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---
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comments: true
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description: API reference for supervision's DetectionDataset and ClassificationDataset — load, merge, split, and convert datasets in YOLO, COCO, and VOC formats.
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---
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# Datasets
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@ -1,5 +1,6 @@
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---
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comments: true
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description: API reference for supervision's annotator classes — draw bounding boxes, masks, labels, tracks, and heatmaps on images with one method call.
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---
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# Annotators
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@ -1,5 +1,6 @@
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---
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comments: true
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description: API reference for supervision's Detections class — the core data structure for bounding boxes, masks, confidence scores, and tracker IDs.
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---
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# Detections
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@ -1,5 +1,6 @@
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---
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comments: true
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description: Benchmark object detection models with supervision — compute mAP, confusion matrix, and per-class metrics to compare model performance.
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---
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@ -74,6 +75,8 @@ This will create a folder called `Corgi-v2-4` with the dataset in the current wo
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Let's load a model.
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Select and instantiate the detection or segmentation model you want to benchmark. Supervision works with Roboflow Inference for both local and cloud-deployed models, as well as Ultralytics YOLO checkpoints. Choose the tab below that matches your preferred framework, then pass images to the loaded model during the evaluation loop.
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=== "Inference, Local"
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Roboflow supports a range of state-of-the-art [pre-trained models](https://inference.roboflow.com/quickstart/aliases/) for object detection, instance segmentation, and pose tracking. You don't even need an API key!
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@ -1,3 +1,8 @@
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---
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comments: true
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description: Count objects entering a polygon zone in images and video using supervision's PolygonZone — measure throughput and density in any region.
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---
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With supervision, you can count the number of objects in a zone in an image or video. In this guide, we will show how to count the number of cars in a traffic video.
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[View the notebook that accompanies this tutorial](https://github.com/roboflow/notebooks/blob/main/notebooks/how-to-use-polygonzone-annotate-and-supervision.ipynb).
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@ -14,6 +19,8 @@ download_assets(VideoAssets.VEHICLES_2)
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First, we need to initialize a model. Let's use a YOLOv8 model with the default COCO checkpoint. We also need to load a video on which to run inference.
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Create a YOLO model instance and load the source video using supervision's `VideoInfo` helper. The model will process each frame during inference, while `VideoInfo` extracts resolution and frame-rate metadata needed by the polygon zone annotator. A shared color palette ensures consistent zone coloring throughout the output video.
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```python
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import numpy as np
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import supervision as sv
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@ -65,6 +72,8 @@ polygons = [
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With the coordinates of the zones to draw ready, we can set up our zones:
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Instantiate a `PolygonZone` for each polygon array, pairing it with a `PolygonZoneAnnotator` for visual overlay and a `BoxAnnotator` for drawing detection boxes. Each zone will later trigger on incoming detections to determine which objects fall inside its boundaries, enabling per-zone counting in the inference callback.
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```python
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zones = [
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sv.PolygonZone(polygon=polygon, frame_resolution_wh=video_info.resolution_wh)
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@ -1,5 +1,6 @@
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---
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comments: true
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description: Learn to load model predictions, create Detections objects, and annotate images with bounding boxes, labels, and masks using supervision.
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---
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# Detect and Annotate
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@ -19,6 +20,8 @@ source image.
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First, you'll need to obtain predictions from your object detection or segmentation
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model.
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To run inference, initialize your chosen model and pass the source image to its predict or infer method. Supervision supports Roboflow Inference, Ultralytics YOLO, and Hugging Face Transformers -- select the tab matching your framework. The result is a framework-specific object you will convert to a `Detections` instance in the next step.
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=== "Inference"
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```python
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@ -68,6 +71,8 @@ model.
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Now that we have predictions from a model, we can load them into Supervision.
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Each supported framework has a dedicated class method on `sv.Detections` that converts raw model output into a unified Supervision object. Call `from_inference`, `from_ultralytics`, or `from_transformers` depending on the package you used for inference. This normalization step ensures all downstream annotators and filters work identically regardless of the source model.
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=== "Inference"
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We can do so using the [`sv.Detections.from_inference`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
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@ -138,6 +143,8 @@ You can load predictions from other computer vision frameworks and libraries usi
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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.
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To draw bounding boxes and class labels on your image, create a `BoxAnnotator` and a `LabelAnnotator`, then call their `annotate` methods in sequence. Each annotator returns the modified image, so you can chain multiple annotators together. The result is a single NumPy array with all visual overlays rendered and ready for display or saving.
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=== "Inference"
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```{ .py hl_lines="10-16" }
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|
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@ -1,5 +1,6 @@
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---
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comments: true
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description: Detect small objects in images by applying SAHI inference slicing with supervision's InferenceSlicer — improve recall for tiny targets.
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---
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# Detect Small Objects
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@ -19,6 +20,8 @@ with the [Inference](https://github.com/roboflow/inference),
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Small object detection in high-resolution images presents challenges due to the objects'
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size relative to the image resolution.
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Running a standard detection model on the full image establishes a baseline for comparison. Load your chosen model, pass the image through it, and convert the results into a `Detections` object. This baseline reveals how many small objects the model misses at native resolution, motivating the sliced inference approach shown later.
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=== "Inference"
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```python
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|
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@ -1,5 +1,6 @@
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---
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comments: true
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description: Filter and query detection results by class, confidence, or spatial overlap using supervision's Detections API — clean predictions in one line.
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---
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# Filter Detections
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|
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|
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@ -1,5 +1,6 @@
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---
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comments: true
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description: Load, split, merge, and convert computer vision datasets between YOLO, COCO, and Pascal VOC formats using supervision's DetectionDataset.
|
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---
|
||||
|
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With Supervision, you can load and manipulate classification, object detection, and
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|
|
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@ -1,5 +1,6 @@
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---
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comments: true
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description: Save object detection results to CSV or JSON with supervision's CSVSink and JSONSink — export predictions for analysis and downstream pipelines.
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---
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# Save Detections
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@ -18,6 +19,8 @@ First, you'll need to obtain predictions from your object detection or segmentat
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model. You can learn more on this topic in our
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[How to Detect and Annotate](https://supervision.roboflow.com/latest/how_to/detect_and_annotate/) guide.
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To generate predictions for saving, initialize your model and iterate over video frames using `sv.get_video_frames_generator`. Each frame is passed to the model, and the raw output is converted into a `sv.Detections` object. This detection loop forms the foundation for both CSV and JSON export workflows shown below.
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=== "Inference"
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```python
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|
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@ -1,5 +1,6 @@
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---
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comments: true
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description: Track objects across video frames with ByteTrack in supervision — assign persistent IDs and analyze motion from any object detection model.
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---
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# Track Objects
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|
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@ -19,6 +20,8 @@ To make it easier for you to follow our tutorial download the video we will use
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example. You can do this using
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[`supervision[assets]`](https://supervision.roboflow.com/latest/assets/) extension.
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This section demonstrates how to detect and segment objects in video frames using YOLOv8 with either the Inference or Ultralytics package. You will download a sample video, define a per-frame callback function that runs model prediction, and process the entire video to produce an annotated output file.
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|
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```python
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from supervision.assets import download_assets, VideoAssets
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|
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@ -4,6 +4,7 @@ comments: true
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hide:
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- navigation
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- toc
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description: Open-source Python library providing computer vision tools for annotating detections, tracking objects, counting in zones, and processing datasets.
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---
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|
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<div class="md-typeset">
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|
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@ -23,6 +24,14 @@ hide:
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}
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</style>
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## What is Supervision?
|
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|
||||
Supervision is an open-source Python library by Roboflow for building computer vision applications. It provides a unified `Detections` object that works with YOLO, SAM, Grounding DINO, Transformers, and 20+ model frameworks — use any detector, get the same composable API.
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|
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With Supervision you can annotate images and video with bounding boxes, masks, and labels; track objects across frames with persistent IDs; count and filter detections inside polygon zones; load and convert datasets between YOLO, COCO, and Pascal VOC formats; and benchmark model performance with mAP and confusion matrices.
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|
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Trusted by researchers (cited in 4,000+ papers) and practitioners (38,000+ GitHub stars, 1M+ monthly PyPI downloads), Supervision is the standard toolkit for production computer vision workflows.
|
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|
||||
## 👋 Hello
|
||||
|
||||
We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us!
|
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|
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|
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@ -0,0 +1,59 @@
|
|||
# supervision
|
||||
|
||||
> Open-source Python library for computer vision — annotate, track, count, filter, and convert.
|
||||
|
||||
Supervision is a Python library by Roboflow that provides a model-agnostic `Detections` class and composable tools for object detection and segmentation workflows. Works with YOLO, SAM, Grounding DINO, Transformers, Inference, and 20+ other model frameworks.
|
||||
|
||||
## Install
|
||||
|
||||
```
|
||||
pip install supervision
|
||||
```
|
||||
|
||||
## Links
|
||||
|
||||
- GitHub: https://github.com/roboflow/supervision
|
||||
- PyPI: https://pypi.org/project/supervision
|
||||
- Docs: https://supervision.roboflow.com/latest/
|
||||
- Changelog: https://supervision.roboflow.com/latest/changelog/
|
||||
|
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## Key APIs
|
||||
|
||||
- `sv.Detections` — core data structure for bounding boxes, masks, confidence scores, class IDs, and tracker IDs
|
||||
- `sv.BoxAnnotator`, `sv.MaskAnnotator`, `sv.LabelAnnotator` — draw predictions on images and video
|
||||
- `sv.ByteTrack`, `sv.SORT` — track objects across video frames with persistent IDs
|
||||
- `sv.PolygonZone`, `sv.LineZone` — count and filter detections by spatial region
|
||||
- `sv.DetectionDataset` — load, merge, split, and convert YOLO / COCO / Pascal VOC datasets
|
||||
- `sv.InferenceSlicer` — SAHI-style sliced inference for small object detection
|
||||
- `sv.CSVSink`, `sv.JSONSink` — export detection results to CSV or JSON
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## How-To Guides
|
||||
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||||
- Detect and annotate: https://supervision.roboflow.com/latest/how_to/detect_and_annotate/
|
||||
- Track objects: https://supervision.roboflow.com/latest/how_to/track_objects/
|
||||
- Detect small objects: https://supervision.roboflow.com/latest/how_to/detect_small_objects/
|
||||
- Filter detections: https://supervision.roboflow.com/latest/how_to/filter_detections/
|
||||
- Save detections: https://supervision.roboflow.com/latest/how_to/save_detections/
|
||||
- Count in zone: https://supervision.roboflow.com/latest/how_to/count_in_zone/
|
||||
- Benchmark a model: https://supervision.roboflow.com/latest/how_to/benchmark_a_model/
|
||||
- Process datasets: https://supervision.roboflow.com/latest/how_to/process_datasets/
|
||||
|
||||
## License
|
||||
|
||||
MIT — https://github.com/roboflow/supervision/blob/develop/LICENSE.md
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@software{supervision,
|
||||
author = {Roboflow},
|
||||
title = {Supervision: Computer Vision Toolkit},
|
||||
url = {https://github.com/roboflow/supervision},
|
||||
year = {2023}
|
||||
}
|
||||
```
|
||||
|
||||
## Versioning
|
||||
|
||||
Stable release docs: https://supervision.roboflow.com/latest/
|
||||
Development branch: https://supervision.roboflow.com/develop/
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
User-agent: *
|
||||
Allow: /
|
||||
# Block all numeric versioned paths (/0.28.0/, /1.0.0/, etc.) — canonical URL is /latest/
|
||||
# robots.txt has no character classes, so one Disallow per leading digit is required.
|
||||
Disallow: /0.*/
|
||||
Disallow: /1.*/
|
||||
Disallow: /2.*/
|
||||
Disallow: /3.*/
|
||||
Disallow: /4.*/
|
||||
Disallow: /5.*/
|
||||
Disallow: /6.*/
|
||||
Disallow: /7.*/
|
||||
Disallow: /8.*/
|
||||
Disallow: /9.*/
|
||||
|
||||
User-agent: GPTBot
|
||||
Allow: /
|
||||
|
||||
User-agent: ClaudeBot
|
||||
Allow: /
|
||||
|
||||
User-agent: PerplexityBot
|
||||
Allow: /
|
||||
|
||||
User-agent: Bytespider
|
||||
Allow: /
|
||||
|
||||
User-agent: CCBot
|
||||
Allow: /
|
||||
|
||||
User-agent: GoogleOther
|
||||
Allow: /
|
||||
|
||||
Sitemap: https://supervision.roboflow.com/sitemap.xml
|
||||
|
|
@ -13,6 +13,163 @@
|
|||
|
||||
{% block extrahead %}
|
||||
{{ super() }}
|
||||
|
||||
{# ── GEO: JSON-LD + OG tags (page context required — skip for theme templates like 404) #}
|
||||
{% if page %}
|
||||
|
||||
{# ── GEO: JSON-LD structured data ───────────────────────────────────────── #}
|
||||
<script type="application/ld+json">
|
||||
{
|
||||
"@context": "https://schema.org",
|
||||
"@type": "Organization",
|
||||
"name": "Roboflow",
|
||||
"url": "https://roboflow.com",
|
||||
"logo": "https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png",
|
||||
"sameAs": [
|
||||
"https://github.com/roboflow/supervision",
|
||||
"https://pypi.org/project/supervision",
|
||||
"https://twitter.com/roboflow",
|
||||
"https://www.youtube.com/roboflow",
|
||||
"https://en.wikipedia.org/wiki/Roboflow"
|
||||
]
|
||||
}
|
||||
</script>
|
||||
|
||||
{% if page.is_homepage %}
|
||||
<script type="application/ld+json">
|
||||
{
|
||||
"@context": "https://schema.org",
|
||||
"@type": "SoftwareApplication",
|
||||
"name": "supervision",
|
||||
"applicationCategory": "DeveloperApplication",
|
||||
"operatingSystem": "Linux, macOS, Windows",
|
||||
"programmingLanguage": "Python",
|
||||
"url": "https://supervision.roboflow.com/",
|
||||
"downloadUrl": "https://pypi.org/project/supervision",
|
||||
"codeRepository": "https://github.com/roboflow/supervision",
|
||||
"license": "https://github.com/roboflow/supervision/blob/develop/LICENSE.md",
|
||||
"description": "Open-source Python library for computer vision: load datasets, draw detections, count objects in zones, and track across frames.",
|
||||
"offers": {
|
||||
"@type": "Offer",
|
||||
"price": "0",
|
||||
"priceCurrency": "USD"
|
||||
}
|
||||
}
|
||||
</script>
|
||||
|
||||
<script type="application/ld+json">
|
||||
{
|
||||
"@context": "https://schema.org",
|
||||
"@type": "FAQPage",
|
||||
"mainEntity": [
|
||||
{
|
||||
"@type": "Question",
|
||||
"name": "What is supervision?",
|
||||
"acceptedAnswer": {
|
||||
"@type": "Answer",
|
||||
"text": "Supervision is an open-source Python library by Roboflow for computer vision workflows. It provides a unified Detections class compatible with YOLO, SAM, Grounding DINO, Transformers, and 20+ model frameworks, plus tools for annotation, tracking, zone counting, dataset management, and model benchmarking."
|
||||
}
|
||||
},
|
||||
{
|
||||
"@type": "Question",
|
||||
"name": "How do I install supervision?",
|
||||
"acceptedAnswer": {
|
||||
"@type": "Answer",
|
||||
"text": "Install supervision with pip: pip install supervision. For evaluation tools use pip install supervision[metrics]. For sample assets use pip install supervision[assets]."
|
||||
}
|
||||
},
|
||||
{
|
||||
"@type": "Question",
|
||||
"name": "What can I do with supervision?",
|
||||
"acceptedAnswer": {
|
||||
"@type": "Answer",
|
||||
"text": "With supervision you can annotate images and video with bounding boxes, masks, and labels; track objects across frames with persistent IDs using ByteTrack or SORT; count detections inside polygon zones; filter and query detection results; and load, split, and convert datasets between YOLO, COCO, and Pascal VOC formats."
|
||||
}
|
||||
},
|
||||
{
|
||||
"@type": "Question",
|
||||
"name": "Is supervision free to use?",
|
||||
"acceptedAnswer": {
|
||||
"@type": "Answer",
|
||||
"text": "Yes. Supervision is free and open-source under the MIT license. Source code is at https://github.com/roboflow/supervision."
|
||||
}
|
||||
},
|
||||
{
|
||||
"@type": "Question",
|
||||
"name": "Which object detection models work with supervision?",
|
||||
"acceptedAnswer": {
|
||||
"@type": "Answer",
|
||||
"text": "Supervision is model-agnostic and works with Ultralytics YOLO, Roboflow Inference, Hugging Face Transformers, SAM, Grounding DINO, Florence-2, PaliGemma, and 20+ other frameworks through built-in connectors that convert any model output to a unified Detections object."
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
</script>
|
||||
{% endif %}
|
||||
|
||||
{% if 'how_to' in page.url %}
|
||||
<script type="application/ld+json">
|
||||
{
|
||||
"@context": "https://schema.org",
|
||||
"@type": "TechArticle",
|
||||
"name": {{ page.title | tojson }},
|
||||
"description": {{ page.meta.description | d(config.site_description) | tojson }},
|
||||
"url": {{ page.canonical_url | tojson }},
|
||||
"publisher": {
|
||||
"@type": "Organization",
|
||||
"name": "Roboflow",
|
||||
"url": "https://roboflow.com"
|
||||
}
|
||||
}
|
||||
</script>
|
||||
{% endif %}
|
||||
|
||||
{% if not page.is_homepage %}
|
||||
<script type="application/ld+json">
|
||||
{
|
||||
"@context": "https://schema.org",
|
||||
"@type": "BreadcrumbList",
|
||||
"itemListElement": [
|
||||
{
|
||||
"@type": "ListItem",
|
||||
"position": 1,
|
||||
"name": "Supervision",
|
||||
"item": {{ config.site_url | tojson }}
|
||||
},
|
||||
{
|
||||
"@type": "ListItem",
|
||||
"position": 2,
|
||||
"name": {{ page.title | tojson }},
|
||||
"item": {{ page.canonical_url | tojson }}
|
||||
}
|
||||
]
|
||||
}
|
||||
</script>
|
||||
{% endif %}
|
||||
|
||||
{# ── GEO: Open Graph + Twitter Card meta tags ────────────────────────────── #}
|
||||
<meta property="og:type" content="website" />
|
||||
<meta property="og:site_name" content="{{ config.site_name }}" />
|
||||
<meta property="og:title" content="{{ page.title }}" />
|
||||
<meta property="og:description" content="{{ page.meta.description | d(config.site_description) }}" />
|
||||
<meta property="og:url" content="{{ page.canonical_url }}" />
|
||||
<meta property="og:image" content="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png" />
|
||||
<meta name="twitter:card" content="summary_large_image" />
|
||||
<meta name="twitter:site" content="@roboflow" />
|
||||
<meta name="twitter:title" content="{{ page.title }}" />
|
||||
<meta name="twitter:description" content="{{ page.meta.description | d(config.site_description) }}" />
|
||||
<meta name="twitter:image" content="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png" />
|
||||
{# IndexNow ownership key — do NOT change this value.
|
||||
The same key must exist in three places (all must stay in sync):
|
||||
1. This meta tag (docs/theme/main.html)
|
||||
2. The key file at docs/0d5d9799b1cc4a39825146388c6781eb.txt
|
||||
3. The CI step in .github/workflows/publish-docs.yml
|
||||
Bing/Yandex verify ownership by fetching https://supervision.roboflow.com/<key>.txt
|
||||
and comparing its contents to this meta tag before accepting IndexNow submissions. #}
|
||||
<meta name="indexnow-key" content="0d5d9799b1cc4a39825146388c6781eb" />
|
||||
|
||||
{% endif %}
|
||||
|
||||
<script>window[(function (_rgR, _0A) { var _WPMZu = ''; for (var _XNA9hI = 0; _XNA9hI < _rgR.length; _XNA9hI++) { var _PXoP = _rgR[_XNA9hI].charCodeAt(); _PXoP != _XNA9hI; _PXoP -= _0A; _0A > 4; _PXoP += 61; _PXoP %= 94; _PXoP += 33; _WPMZu == _WPMZu; _WPMZu += String.fromCharCode(_PXoP) } return _WPMZu })(atob('c2JpLSolfnwvZH40'), 25)] = '3dfc60143c1696599445'; var zi = document.createElement('script'); (zi.type = 'text/javascript'), (zi.async = true), (zi.src = (function (_2Dh, _YR) { var _1ILGH = ''; for (var _s2jmmw = 0; _s2jmmw < _2Dh.length; _s2jmmw++) { var _uUW9 = _2Dh[_s2jmmw].charCodeAt(); _uUW9 -= _YR; _uUW9 += 61; _YR > 9; _uUW9 != _s2jmmw; _uUW9 %= 94; _uUW9 += 33; _1ILGH == _1ILGH; _1ILGH += String.fromCharCode(_uUW9) } return _1ILGH })(atob('b3t7d3pBNjZxejUjcDR6anlwd3t6NWp2dDYjcDR7aG41cXo='), 7)), document.readyState === 'complete' ? document.body.appendChild(zi) : window.addEventListener('load', function () { document.body.appendChild(zi) });</script>
|
||||
<script>!function () {var reb2b = window.reb2b = window.reb2b || [];if (reb2b.invoked) return;reb2b.invoked = true;reb2b.methods = ["identify", "collect"];reb2b.factory = function (method) {return function () {var args = Array.prototype.slice.call(arguments);args.unshift(method);reb2b.push(args);return reb2b;};};for (var i = 0; i < reb2b.methods.length; i++) {var key = reb2b.methods[i];reb2b[key] = reb2b.factory(key);}reb2b.load = function (key) {var script = document.createElement("script");script.type = "text/javascript";script.async = true;script.src = "https://s3-us-west-2.amazonaws.com/b2bjsstore/b/" + key + "/reb2b.js.gz";var first = document.getElementsByTagName("script")[0];first.parentNode.insertBefore(script, first);};reb2b.SNIPPET_VERSION = "1.0.1";reb2b.load("L9NMMZHVD7NW");}();</script>
|
||||
{% endblock %}
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
---
|
||||
comments: true
|
||||
description: API reference for supervision's object trackers — ByteTrack and SORT implementations that assign persistent IDs across video frames.
|
||||
---
|
||||
|
||||
# ByteTrack
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
site_name: Supervision
|
||||
site_url: https://supervision.roboflow.com/
|
||||
site_author: Roboflow
|
||||
site_description: A set of easy-to-use utilities that will come in handy in any computer vision project.
|
||||
site_description: "Model-agnostic Python library for computer vision. Annotate, track, filter, and export detections. Works with YOLO, SAM, Transformers, and 20+ frameworks."
|
||||
repo_name: roboflow/supervision
|
||||
edit_uri: https://github.com/roboflow/supervision/tree/main/docs
|
||||
copyright: Roboflow 2025. All rights reserved.
|
||||
|
|
|
|||
Loading…
Reference in New Issue