[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72020":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":33,"readmeContent":34,"aiSummary":35,"trendingCount":16,"starSnapshotCount":16,"syncStatus":36,"lastSyncTime":37,"discoverSource":38},72020,"rf-detr","roboflow\u002Frf-detr","roboflow","RF-DETR is a real-time object detection and segmentation model architecture developed by Roboflow, SOTA on COCO, designed for fine-tuning. [ICLR 2026] ","https:\u002F\u002Frfdetr.roboflow.com",null,"Python",7641,975,68,71,0,59,161,606,177,39.97,"Apache License 2.0",false,"develop",true,[27,28,29,30,31,5,32],"computer-vision","detr","instance-segmentation","machine-learning","object-detection","sota","2026-06-12 02:02:57","# RF-DETR: Real-Time SOTA Detection and Segmentation\n\n[![version](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Frfdetr.svg)](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Frfdetr)\n[![downloads](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Frfdetr)](https:\u002F\u002Fpypistats.org\u002Fpackages\u002Frfdetr)\n[![codecov](https:\u002F\u002Fcodecov.io\u002Fgh\u002Froboflow\u002Frf-detr\u002Fgraph\u002Fbadge.svg?token=K8V4ARR3XV)](https:\u002F\u002Fcodecov.io\u002Fgh\u002Froboflow\u002Frf-detr)\n[![python-version](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Frfdetr)](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Frfdetr)\n[![license](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-Apache%202.0-blue)](https:\u002F\u002Fgithub.com\u002Froboflow\u002Frfdetr\u002Fblob\u002Fmain\u002FLICENSE)\n\n[![arXiv](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FarXiv-2511.09554-b31b1b.svg)](https:\u002F\u002Farxiv.org\u002Fabs\u002F2511.09554)\n[![hf space](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FSkalskiP\u002FRF-DETR)\n[![colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Froboflow-ai\u002Fnotebooks\u002Fblob\u002Fmain\u002Fnotebooks\u002Fhow-to-finetune-rf-detr-on-detection-dataset.ipynb)\n[![roboflow](https:\u002F\u002Fraw.githubusercontent.com\u002Froboflow-ai\u002Fnotebooks\u002Fmain\u002Fassets\u002Fbadges\u002Froboflow-blogpost.svg)](https:\u002F\u002Fblog.roboflow.com\u002Frf-detr)\n[![discord](https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1159501506232451173?logo=discord&label=discord&labelColor=fff&color=5865f2&link=https%3A%2F%2Fdiscord.gg%2FGbfgXGJ8Bk)](https:\u002F\u002Fdiscord.gg\u002FGbfgXGJ8Bk)\n\nRF-DETR is a real-time transformer architecture for object detection and instance segmentation developed by Roboflow. Built on a DINOv2 vision transformer backbone, RF-DETR delivers state-of-the-art accuracy and latency trade-offs on [Microsoft COCO](https:\u002F\u002Fcocodataset.org\u002F#home) and [RF100-VL](https:\u002F\u002Fgithub.com\u002Froboflow\u002Frf100-vl).\n\nRF-DETR uses a DINOv2 vision transformer backbone and supports both detection and instance segmentation in a single, consistent API. The open-source `rfdetr` package and Apache-designated models are released under Apache 2.0, while Plus components (`rfdetr_plus`, including RF-DETR-XL\u002F2XL detection models) are licensed under PML 1.0.\n\nhttps:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Fadd23fd1-266f-4538-8809-d7dd5767e8e6\n\n## Install\n\nTo install RF-DETR, install the `rfdetr` package in a [**Python>=3.10**](https:\u002F\u002Fwww.python.org\u002F) environment with `pip`.\n\n```bash\npip install rfdetr\n```\n\n\u003Cdetails>\n\u003Csummary>Install from source\u003C\u002Fsummary>\n\n\u003Cbr>\n\nBy installing RF-DETR from source, you can explore the most recent features and enhancements that have not yet been officially released. **Please note that these updates are still in development and may not be as stable as the latest published release.**\n\n```bash\npip install https:\u002F\u002Fgithub.com\u002Froboflow\u002Frf-detr\u002Farchive\u002Frefs\u002Fheads\u002Fdevelop.zip\n```\n\n\u003C\u002Fdetails>\n\n## Benchmarks\n\nRF-DETR achieves state-of-the-art results in both object detection and instance segmentation, with benchmarks reported on Microsoft COCO and RF100-VL. The charts and tables below compare RF-DETR against other top real-time models across accuracy and latency for detection and segmentation. All latency numbers were measured on an NVIDIA T4 using TensorRT, FP16, and batch size 1. For full benchmarking methodology and reproducibility details, see [roboflow\u002Fsab](https:\u002F\u002Fgithub.com\u002Froboflow\u002Fsingle_artifact_benchmarking).\n\n### Detection\n\n\u003Cimg alt=\"rf_detr_1-4_latency_accuracy_object_detection\" src=\"https:\u002F\u002Fstorage.googleapis.com\u002Fcom-roboflow-marketing\u002Frf-detr\u002Frf_detr_1-4_latency_accuracy_object_detection.png\" \u002F>\n\n\u003Cdetails>\n\u003Csummary>See object detection benchmark numbers\u003C\u002Fsummary>\n\n\u003Cbr>\n\n| Architecture  | COCO AP\u003Csub>50\u003C\u002Fsub> | COCO AP\u003Csub>50:95\u003C\u002Fsub> | RF100VL AP\u003Csub>50\u003C\u002Fsub> | RF100VL AP\u003Csub>50:95\u003C\u002Fsub> | Latency (ms) | Params (M) | Resolution |  License   |\n| :-----------: | :------------------: | :---------------------: | :---------------------: | :------------------------: | :----------: | :--------: | :--------: | :--------: |\n|   RF-DETR-N   |         67.6         |          48.4           |          85.0           |            57.7            |     2.3      |    30.5    |  384x384   | Apache 2.0 |\n|   RF-DETR-S   |         72.1         |          53.0           |          86.7           |            60.2            |     3.5      |    32.1    |  512x512   | Apache 2.0 |\n|   RF-DETR-M   |         73.6         |          54.7           |          87.4           |            61.2            |     4.4      |    33.7    |  576x576   | Apache 2.0 |\n|   RF-DETR-L   |         75.1         |          56.5           |          88.2           |            62.2            |     6.8      |    33.9    |  704x704   | Apache 2.0 |\n| RF-DETR-XL △  |         77.4         |          58.6           |          88.5           |            62.9            |     11.5     |   126.4    |  700x700   |  PML 1.0   |\n| RF-DETR-2XL △ |         78.5         |          60.1           |          89.0           |            63.2            |     17.2     |   126.9    |  880x880   |  PML 1.0   |\n|   YOLO11-N    |         52.0         |          37.4           |          81.4           |            55.3            |     2.5      |    2.6     |  640x640   |  AGPL-3.0  |\n|   YOLO11-S    |         59.7         |          44.4           |          82.3           |            56.2            |     3.2      |    9.4     |  640x640   |  AGPL-3.0  |\n|   YOLO11-M    |         64.1         |          48.6           |          82.5           |            56.5            |     5.1      |    20.1    |  640x640   |  AGPL-3.0  |\n|   YOLO11-L    |         64.9         |          49.9           |          82.2           |            56.5            |     6.5      |    25.3    |  640x640   |  AGPL-3.0  |\n|   YOLO11-X    |         66.1         |          50.9           |          81.7           |            56.2            |     10.5     |    56.9    |  640x640   |  AGPL-3.0  |\n|   YOLO26-N    |         55.8         |          40.3           |          76.7           |            52.0            |     1.7      |    2.6     |  640x640   |  AGPL-3.0  |\n|   YOLO26-S    |         64.3         |          47.7           |          82.7           |            57.0            |     2.6      |    9.4     |  640x640   |  AGPL-3.0  |\n|   YOLO26-M    |         69.7         |          52.5           |          84.4           |            58.7            |     4.4      |    20.1    |  640x640   |  AGPL-3.0  |\n|   YOLO26-L    |         71.1         |          54.1           |          85.0           |            59.3            |     5.7      |    25.3    |  640x640   |  AGPL-3.0  |\n|   YOLO26-X    |         74.0         |          56.9           |          85.6           |            60.0            |     9.6      |    56.9    |  640x640   |  AGPL-3.0  |\n|   LW-DETR-T   |         60.7         |          42.9           |          84.7           |            57.1            |     1.9      |    12.1    |  640x640   | Apache 2.0 |\n|   LW-DETR-S   |         66.8         |          48.0           |          85.0           |            57.4            |     2.6      |    14.6    |  640x640   | Apache 2.0 |\n|   LW-DETR-M   |         72.0         |          52.6           |          86.8           |            59.8            |     4.4      |    28.2    |  640x640   | Apache 2.0 |\n|   LW-DETR-L   |         74.6         |          56.1           |          87.4           |            61.5            |     6.9      |    46.8    |  640x640   | Apache 2.0 |\n|   LW-DETR-X   |         76.9         |          58.3           |          87.9           |            62.1            |     13.0     |   118.0    |  640x640   | Apache 2.0 |\n|   D-FINE-N    |         60.2         |          42.7           |          84.4           |            58.2            |     2.1      |    3.8     |  640x640   | Apache 2.0 |\n|   D-FINE-S    |         67.6         |          50.6           |          85.3           |            60.3            |     3.5      |    10.2    |  640x640   | Apache 2.0 |\n|   D-FINE-M    |         72.6         |          55.0           |          85.5           |            60.6            |     5.4      |    19.2    |  640x640   | Apache 2.0 |\n|   D-FINE-L    |         74.9         |          57.2           |          86.4           |            61.6            |     7.5      |    31.0    |  640x640   | Apache 2.0 |\n|   D-FINE-X    |         76.8         |          59.3           |          86.9           |            62.2            |     11.5     |    62.0    |  640x640   | Apache 2.0 |\n\n\u003C\u002Fdetails>\n\n### Segmentation\n\n\u003Cimg alt=\"rf_detr_1-4_latency_accuracy_instance_segmentation\" src=\"https:\u002F\u002Fstorage.googleapis.com\u002Fcom-roboflow-marketing\u002Frf-detr\u002Frf_detr_1-4_latency_accuracy_instance_segmentation.png\" \u002F>\n\n\u003Cdetails>\n\u003Csummary>See instance segmentation benchmark numbers\u003C\u002Fsummary>\n\n\u003Cbr>\n\n|  Architecture   | COCO AP\u003Csub>50\u003C\u002Fsub> | COCO AP\u003Csub>50:95\u003C\u002Fsub> | Latency (ms) | Params (M) | Resolution |  License   |\n| :-------------: | :------------------: | :---------------------: | :----------: | :--------: | :--------: | :--------: |\n|  RF-DETR-Seg-N  |         63.0         |          40.3           |     3.4      |    33.6    |  312x312   | Apache 2.0 |\n|  RF-DETR-Seg-S  |         66.2         |          43.1           |     4.4      |    33.7    |  384x384   | Apache 2.0 |\n|  RF-DETR-Seg-M  |         68.4         |          45.3           |     5.9      |    35.7    |  432x432   | Apache 2.0 |\n|  RF-DETR-Seg-L  |         70.5         |          47.1           |     8.8      |    36.2    |  504x504   | Apache 2.0 |\n| RF-DETR-Seg-XL  |         72.2         |          48.8           |     13.5     |    38.1    |  624x624   | Apache 2.0 |\n| RF-DETR-Seg-2XL |         73.1         |          49.9           |     21.8     |    38.6    |  768x768   | Apache 2.0 |\n|  YOLOv8-N-Seg   |         45.6         |          28.3           |     3.5      |    3.4     |  640x640   |  AGPL-3.0  |\n|  YOLOv8-S-Seg   |         53.8         |          34.0           |     4.2      |    11.8    |  640x640   |  AGPL-3.0  |\n|  YOLOv8-M-Seg   |         58.2         |          37.3           |     7.0      |    27.3    |  640x640   |  AGPL-3.0  |\n|  YOLOv8-L-Seg   |         60.5         |          39.0           |     9.7      |    46.0    |  640x640   |  AGPL-3.0  |\n|  YOLOv8-XL-Seg  |         61.3         |          39.5           |     14.0     |    71.8    |  640x640   |  AGPL-3.0  |\n|  YOLOv11-N-Seg  |         47.8         |          30.0           |     3.6      |    2.9     |  640x640   |  AGPL-3.0  |\n|  YOLOv11-S-Seg  |         55.4         |          35.0           |     4.6      |    10.1    |  640x640   |  AGPL-3.0  |\n|  YOLOv11-M-Seg  |         60.0         |          38.5           |     6.9      |    22.4    |  640x640   |  AGPL-3.0  |\n|  YOLOv11-L-Seg  |         61.5         |          39.5           |     8.3      |    27.6    |  640x640   |  AGPL-3.0  |\n| YOLOv11-XL-Seg  |         62.4         |          40.1           |     13.7     |    62.1    |  640x640   |  AGPL-3.0  |\n|  YOLO26-N-Seg   |         54.3         |          34.7           |     2.31     |    2.7     |  640x640   |  AGPL-3.0  |\n|  YOLO26-S-Seg   |         62.4         |          40.2           |     3.47     |    10.4    |  640x640   |  AGPL-3.0  |\n|  YOLO26-M-Seg   |         67.8         |          44.0           |     6.32     |    23.6    |  640x640   |  AGPL-3.0  |\n|  YOLO26-L-Seg   |         69.8         |          45.5           |     7.58     |    28.0    |  640x640   |  AGPL-3.0  |\n|  YOLO26-X-Seg   |         71.6         |          46.8           |    12.92     |    62.8    |  640x640   |  AGPL-3.0  |\n\n\u003C\u002Fdetails>\n\n## Run Models\n\n### Detection\n\nRF-DETR provides multiple model sizes, ranging from Nano to 2XLarge. To use a different model size, replace the class name in the code snippet below with another class from the table.\n\n```python\nimport supervision as sv\nfrom rfdetr import RFDETRMedium\nfrom rfdetr.assets.coco_classes import COCO_CLASSES\n\nmodel = RFDETRMedium()\n\ndetections = model.predict(\"https:\u002F\u002Fmedia.roboflow.com\u002Fdog.jpg\", threshold=0.5)\n\nlabels = [f\"{COCO_CLASSES[class_id]}\" for class_id in detections.class_id]\n\nannotated_image = sv.BoxAnnotator().annotate(detections.metadata[\"source_image\"], detections)\nannotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels)\n```\n\n\u003Cdetails>\n\u003Csummary>Run RF-DETR with Inference\u003C\u002Fsummary>\n\n\u003Cbr>\n\nYou can also run RF-DETR models using the Inference library. To switch model size, select the appropriate inference package alias from the table below.\n\n```python\nimport requests\nimport supervision as sv\nfrom PIL import Image\nfrom inference import get_model\n\nmodel = get_model(\"rfdetr-medium\")\n\nimage = Image.open(requests.get(\"https:\u002F\u002Fmedia.roboflow.com\u002Fdog.jpg\", stream=True).raw)\npredictions = model.infer(image, confidence=0.5)[0]\ndetections = sv.Detections.from_inference(predictions)\n\nannotated_image = sv.BoxAnnotator().annotate(image, detections)\nannotated_image = sv.LabelAnnotator().annotate(annotated_image, detections)\n```\n\n\u003C\u002Fdetails>\n\n| Size | RF-DETR package class | Inference package alias | COCO AP\u003Csub>50\u003C\u002Fsub> | COCO AP\u003Csub>50:95\u003C\u002Fsub> | Latency (ms) | Params (M) | Resolution |  License   |\n| :--: | :-------------------: | :---------------------- | :------------------: | :---------------------: | :----------: | :--------: | :--------: | :--------: |\n|  N   |     `RFDETRNano`      | `rfdetr-nano`           |         67.6         |          48.4           |     2.3      |    30.5    |  384x384   | Apache 2.0 |\n|  S   |     `RFDETRSmall`     | `rfdetr-small`          |         72.1         |          53.0           |     3.5      |    32.1    |  512x512   | Apache 2.0 |\n|  M   |    `RFDETRMedium`     | `rfdetr-medium`         |         73.6         |          54.7           |     4.4      |    33.7    |  576x576   | Apache 2.0 |\n|  L   |     `RFDETRLarge`     | `rfdetr-large`          |         75.1         |          56.5           |     6.8      |    33.9    |  704x704   | Apache 2.0 |\n|  XL  |   `RFDETRXLarge` △    | `rfdetr-xlarge`         |         77.4         |          58.6           |     11.5     |   126.4    |  700x700   |  PML 1.0   |\n| 2XL  |   `RFDETR2XLarge` △   | `rfdetr-2xlarge`        |         78.5         |          60.1           |     17.2     |   126.9    |  880x880   |  PML 1.0   |\n\n> △ Requires the `rfdetr_plus` extension: `pip install rfdetr[plus]`. See [License](#license) for details.\n\n### Segmentation\n\nRF-DETR supports instance segmentation with model sizes from Nano to 2XLarge. To use a different model size, replace the class name in the code snippet below with another class from the table.\n\n```python\nimport supervision as sv\nfrom rfdetr import RFDETRSegMedium\nfrom rfdetr.assets.coco_classes import COCO_CLASSES\n\nmodel = RFDETRSegMedium()\n\ndetections = model.predict(\"https:\u002F\u002Fmedia.roboflow.com\u002Fdog.jpg\", threshold=0.5)\n\nlabels = [f\"{COCO_CLASSES[class_id]}\" for class_id in detections.class_id]\n\nannotated_image = sv.MaskAnnotator().annotate(detections.metadata[\"source_image\"], detections)\nannotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels)\n```\n\n\u003Cdetails>\n\u003Csummary>Run RF-DETR-Seg with Inference\u003C\u002Fsummary>\n\n\u003Cbr>\n\nYou can also run RF-DETR-Seg models using the Inference library. To switch model size, select the appropriate inference package alias from the table below.\n\n```python\nimport requests\nimport supervision as sv\nfrom PIL import Image\nfrom inference import get_model\n\nmodel = get_model(\"rfdetr-seg-medium\")\n\nimage = Image.open(requests.get(\"https:\u002F\u002Fmedia.roboflow.com\u002Fdog.jpg\", stream=True).raw)\npredictions = model.infer(image, confidence=0.5)[0]\ndetections = sv.Detections.from_inference(predictions)\n\nannotated_image = sv.MaskAnnotator().annotate(image, detections)\nannotated_image = sv.LabelAnnotator().annotate(annotated_image, detections)\n```\n\n\u003C\u002Fdetails>\n\n| Size | RF-DETR package class | Inference package alias | COCO AP\u003Csub>50\u003C\u002Fsub> | COCO AP\u003Csub>50:95\u003C\u002Fsub> | Latency (ms) | Params (M) | Resolution |  License   |\n| :--: | :-------------------: | :---------------------- | :------------------: | :---------------------: | :----------: | :--------: | :--------: | :--------: |\n|  N   |    `RFDETRSegNano`    | `rfdetr-seg-nano`       |         63.0         |          40.3           |     3.4      |    33.6    |  312x312   | Apache 2.0 |\n|  S   |   `RFDETRSegSmall`    | `rfdetr-seg-small`      |         66.2         |          43.1           |     4.4      |    33.7    |  384x384   | Apache 2.0 |\n|  M   |   `RFDETRSegMedium`   | `rfdetr-seg-medium`     |         68.4         |          45.3           |     5.9      |    35.7    |  432x432   | Apache 2.0 |\n|  L   |   `RFDETRSegLarge`    | `rfdetr-seg-large`      |         70.5         |          47.1           |     8.8      |    36.2    |  504x504   | Apache 2.0 |\n|  XL  |   `RFDETRSegXLarge`   | `rfdetr-seg-xlarge`     |         72.2         |          48.8           |     13.5     |    38.1    |  624x624   | Apache 2.0 |\n| 2XL  |  `RFDETRSeg2XLarge`   | `rfdetr-seg-2xlarge`    |         73.1         |          49.9           |     21.8     |    38.6    |  768x768   | Apache 2.0 |\n\n### Train Models\n\nRF-DETR supports training for both object detection and instance segmentation. You can train models in [Google Colab](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Froboflow-ai\u002Fnotebooks\u002Fblob\u002Fmain\u002Fnotebooks\u002Fhow-to-finetune-rf-detr-on-detection-dataset.ipynb) or directly on the Roboflow platform. Below you will find a step-by-step video fine-tuning tutorial.\n\n[![rf-detr-tutorial-banner](https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F555a45c3-96e8-4d8a-ad29-f23403c8edfd)](https:\u002F\u002Fyoutu.be\u002F-OvpdLAElFA)\n\n## Documentation\n\nVisit our [documentation website](https:\u002F\u002Frfdetr.roboflow.com) to learn more about how to use RF-DETR.\n\n## License\n\nLicensing is split by component:\n\n- The open-source `rfdetr` package and Apache-designated model weights are licensed under Apache License 2.0. See [`LICENSE`](LICENSE).\n- Plus components, including the `rfdetr_plus` extension and RF-DETR-XL \u002F RF-DETR-2XL detection models, are licensed under PML 1.0.\n\n## Acknowledgements\n\nOur work is built upon [LW-DETR](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2406.03459), [DINOv2](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.07193), and [Deformable DETR](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.04159). Thanks to their authors for their excellent work!\n\n## Citation\n\nIf you find our work helpful for your research, please consider citing the following BibTeX entry.\n\n```bibtex\n@misc{rf-detr,\n    title={RF-DETR: Neural Architecture Search for Real-Time Detection Transformers},\n    author={Isaac Robinson and Peter Robicheaux and Matvei Popov and Deva Ramanan and Neehar Peri},\n    year={2025},\n    eprint={2511.09554},\n    archivePrefix={arXiv},\n    primaryClass={cs.CV},\n    url={https:\u002F\u002Farxiv.org\u002Fabs\u002F2511.09554},\n}\n```\n\n## Contribute\n\nWe welcome and appreciate all contributions! If you notice any issues or bugs, have questions, or would like to suggest new features, please [open an issue](https:\u002F\u002Fgithub.com\u002Froboflow\u002Frf-detr\u002Fissues\u002Fnew) or pull request. By sharing your ideas and improvements, you help make RF-DETR better for everyone.\n\n\u003Cp align=\"center\">\n    \u003Ca href=\"https:\u002F\u002Fyoutube.com\u002Froboflow\">\u003Cimg src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fnotebooks\u002Ftemplate\u002Ficons\u002Fpurple\u002Fyoutube.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634652\" width=\"3%\"\u002F>\u003C\u002Fa>\n    \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\"\u002F>\n    \u003Ca href=\"https:\u002F\u002Froboflow.com\">\u003Cimg src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fnotebooks\u002Ftemplate\u002Ficons\u002Fpurple\u002Froboflow-app.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949746649\" width=\"3%\"\u002F>\u003C\u002Fa>\n    \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\"\u002F>\n    \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Froboflow-ai\u002F\">\u003Cimg src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fnotebooks\u002Ftemplate\u002Ficons\u002Fpurple\u002Flinkedin.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633691\" width=\"3%\"\u002F>\u003C\u002Fa>\n    \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\"\u002F>\n    \u003Ca href=\"https:\u002F\u002Fdocs.roboflow.com\">\u003Cimg src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fnotebooks\u002Ftemplate\u002Ficons\u002Fpurple\u002Fknowledge.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634511\" width=\"3%\"\u002F>\u003C\u002Fa>\n    \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\"\u002F>\n    \u003Ca href=\"https:\u002F\u002Fdiscuss.roboflow.com\">\u003Cimg src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fnotebooks\u002Ftemplate\u002Ficons\u002Fpurple\u002Fforum.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633584\" width=\"3%\"\u002F>\u003C\u002Fa>\n    \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\"\u002F>\n    \u003Ca href=\"https:\u002F\u002Fblog.roboflow.com\">\u003Cimg src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fnotebooks\u002Ftemplate\u002Ficons\u002Fpurple\u002Fblog.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633605\" width=\"3%\"\u002F>\u003C\u002Fa>\n\u003C\u002Fp>\n","RF-DETR 是由 Roboflow 开发的一个实时物体检测和实例分割模型架构，旨在提供最先进的准确性和低延迟。该项目基于 DINOv2 视觉变换器骨干网络，支持通过一致的 API 进行检测和分割任务，并在 Microsoft COCO 和 RF100-VL 数据集上展示了卓越性能。其开源 `rfdetr` 包遵循 Apache 2.0 许可协议发布，便于用户快速集成到自己的应用中。适合需要高性能、实时处理能力的计算机视觉应用场景，如自动驾驶、安防监控等。",2,"2026-06-11 03:39:59","high_star"]