Update README.md to enhance features and add examples #2065
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| **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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| ## 🌟 Key Features | ||
| - 🚀 **Model Agnostic**: Connectors for Ultralytics YOLO, Transformers, MMDetection, Roboflow Inference & more ([docs](https://supervision.roboflow.com/latest/detection/core/#detections)) | ||
| - 🎨 **25+ Annotators**: Box, Label, Mask, Trace, HeatMap, Icon, Blur, Pixelate, Halo & many others ([docs](https://supervision.roboflow.com/latest/detection/annotators/)) | ||
| - 🏃♂️ **Object Tracking**: ByteTracker & more for multi-object tracking ([docs](https://supervision.roboflow.com/latest/trackers/byte_tracker/)) | ||
| - 📏 **Line & Polygon Zones**: Count/filter objects crossing lines or in polygons | ||
| - 📊 **Metrics**: mAP, Precision, Recall, F1 Score ([docs](https://supervision.roboflow.com/latest/metrics/mean_average_precision/)) | ||
| - 🗂️ **Datasets**: Load, split, convert (COCO, YOLO, Pascal VOC, etc.) ([docs](https://supervision.roboflow.com/latest/datasets/core/)) | ||
| - 🔧 **Utils**: NMS/IoU filters, geometry primitives, drawing helpers | ||
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| ## 🚀 Why Supervision? | ||
| - ⏱️ **Accelerate Development**: Ready-to-use utilities for annotations, tracking, zones, metrics – skip boilerplate code. | ||
| - 🤖 **Model Agnostic**: Integrates seamlessly with YOLO, Transformers, MMDetection, Roboflow Inference & more. | ||
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Collaborator
There was a problem hiding this comment. Let's alweays put Roboflow Inference as #1 when we list frameworks and libraries like this. |
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| - ⚡ **Lightweight**: Minimal deps (numpy, opencv), no ML frameworks needed, optimized for speed. | ||
| - 🏗️ **Production-Ready**: Robust, battle-tested tools used in Roboflow products. | ||
| - 📚 **Rich Ecosystem**: 25+ annotators, full docs, tutorials, notebooks, active Discord ([join here](https://discord.gg/GbfgXGJ8Bk)). | ||
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Collaborator
There was a problem hiding this comment. This feels very AI generated. Let's remove all the emojis other than ones in headers. All headers were lower case, so lets keep things consistant.
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There was a problem hiding this comment. I put all emojis there on purpose; it makes the reading nicer
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There was a problem hiding this comment. it is fine to write it just once |
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| ## 💻 install | ||
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| Pip install the supervision package in a | ||
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| </details> | ||
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| ### 🏃♂️ tracking | ||
| Track objects across frames with state-of-the-art trackers like ByteTrack. | ||
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| ```python | ||
| import supervision as sv | ||
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| byte_tracker = sv.ByteTracker() | ||
| tracks = byte_tracker.update_with_detections(detections=detections) | ||
| ``` | ||
| Use `sv.IdAnnotator()` to visualize track `id`. | ||
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| ### 📏 line zone | ||
| Count objects crossing a line. | ||
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| ```python | ||
| line_zone = sv.LineZone(start=sv.Point(0, 0), end=sv.Point(640, 640)) | ||
| line_zone_annotator = sv.LineZoneAnnotator() | ||
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| line_zone.trigger(detections=detections) | ||
| annotated_frame = line_zone_annotator.annotate(scene=image, line_counter=line_zone) | ||
| ``` | ||
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There was a problem hiding this comment. Tracking is still here, but with next release of trackers that will add |
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| ### 🔶 polygon zone | ||
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Collaborator
There was a problem hiding this comment. let's not go to hard on emojis + keep things consistant
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There was a problem hiding this comment. it is fine to write it just once |
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| Count/filter objects in polygon zone. | ||
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| ```python | ||
| import numpy as np | ||
| polygon = np.array([ | ||
| [0, 0], | ||
| [100, 0], | ||
| [100, 100], | ||
| [0, 100] | ||
| ]) | ||
| polygon_zone = sv.PolygonZone(polygon=polygon) | ||
| polygon_zone_annotator = sv.PolygonZoneAnnotator() | ||
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| polygon_zone.trigger(detections=detections) | ||
| annotated_frame = polygon_zone_annotator.annotate(scene=image, polygon_zone=polygon_zone) | ||
| ``` | ||
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| ### 📊 metrics | ||
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There was a problem hiding this comment. let's not go to hard on emojis + keep things consistant
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There was a problem hiding this comment. it is fine to write it just once |
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| Evaluate detection performance (mAP, etc.). | ||
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| ```python | ||
| from supervision.metrics import MeanAveragePrecision | ||
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| metric = MeanAveragePrecision(class_names=["class1", "class2"]) | ||
| metric.update(predictions=predictions, ground_truths=ground_truths) | ||
| print(metric.result()) | ||
| ``` | ||
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| ## 🎬 tutorials | ||
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| Want to learn how to use Supervision? Explore our [how-to guides](https://supervision.roboflow.com/develop/how_to/detect_and_annotate/), [end-to-end examples](https://github.com/roboflow/supervision/tree/develop/examples), [cheatsheet](https://roboflow.github.io/cheatsheet-supervision/), and [cookbooks](https://supervision.roboflow.com/develop/cookbooks/)! | ||
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| <div> | ||
| <a href="https://youtu.be/hAWpsIuem10" title="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing"><img src="https://github.com/SkalskiP/SkalskiP/assets/26109316/a742823d-c158-407d-b30f-063a5d11b4e1" alt="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing" width="300px" align="left" /></a> | ||
| <a href="https://youtu.be/hAWpsIuem10" title="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing"><strong>Dwell Time Analysis with Computer Vision | Real-Time Stream Processing</strong></a> | ||
| <br> | ||
| Learn how to use computer vision to analyze wait times and optimize processes. This tutorial covers object detection, tracking, and calculating time spent in designated zones. Use these techniques to improve customer experience in retail, traffic management, or other scenarios. | ||
| </div> | ||
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| <div> | ||
| <a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><img src="https://github.com/SkalskiP/SkalskiP/assets/26109316/61a444c8-b135-48ce-b979-2a5ab47c5a91" alt="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source" width="300px" align="left" /></a> | ||
| <a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><strong>Speed Estimation & Vehicle Tracking | Computer Vision | Open Source</strong></a> | ||
| <br> | ||
| Learn how to track and estimate the speed of vehicles using YOLO, ByteTrack, and Roboflow Inference. This comprehensive tutorial covers object detection, multi-object tracking, filtering detections, perspective transformation, speed estimation, visualization improvements, and more. | ||
| </div> | ||
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| ## 💜 built with supervision | ||
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@@ -270,45 +335,28 @@ We love your input! Please see our [contributing guide](https://github.com/robof | |
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| <a href="https://blog.roboflow.com"> | ||
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| src="https://media.roboflow.com/notebooks/template/icons/purple/blog.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633605" width="3%" /> | ||
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| </div> | ||
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Let's alweays put Roboflow Inference as #1 when we list frameworks and libraries like this.