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implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
| Date | Stars |
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| 2026-07-24 | 2002 |
| 2026-07-25 | 2002 |
| 2026-07-28 | 2002 |
| 2026-07-30 | 2002 |
| 2026-08-06 | 2002 |
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# YOLOR implementation of paper - [You Only Learn One Representation: Unified Network for Multiple Tasks](https://arxiv.org/abs/2105.04206) [](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=you-only-learn-one-representation-unified)  <img src="https://github.com/WongKinYiu/yolor/blob/main/figure/performance.png" height="480"> To get the results on the table, please use [this branch](https://github.com/WongKinYiu/yolor/tree/paper). | Model | Test Size | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | batch1 throughput | batch32 inference | | :-- | :-: | :-: | :-: | :-: | :-: | :-: | | **YOLOR-CSP** | 640 | **52.8%** | **71.2%** | **57.6%** | 106 *fps* | 3.2 *ms* | | **YOLOR-CSP-X** | 640 | **54.8%** | **73.1%** | **59.7%** | 87 *fps* | 5.5 *ms* | | **YOLOR-P6** | 1280 | **55.7%** | **73.3%** | **61.0%** | 76 *fps* | 8.3 *ms* | | **YOLOR-W6** | 1280 | **56.9%** | **74.4%** | **62.2%** | 66 *fps* | 10.7 *ms* | | **YOLOR-E6** | 1280 | **57.6%** | **75.2%** | **63.0%** | 45 *fps* | 17.1 *ms* | | **YOLOR-D6** | 1280 | **58.2%** | **75.8%** | **63.8%** | 34 *fps* | 21.8 *ms* | | | | | | | | | | **YOLOv4-P5** | 896 | **51.8%** | **70.3%** | **56.6%** | 41 *fps* (old) | - | | **YOLOv4-P6** | 1280 | **54.5%** | **72.6%** | **59.8%** | 30 *fps* (old) | - | | **YOLOv4-P7** | 1536 | **55.5%** | **73.4%** | **60.8%** | 16 *fps* (old) | - | | | | | | | | | * Fix the speed bottleneck on our NFS, many thanks to NCHC, TWCC, and NARLabs support teams. | Model | Test Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | AP<sub>75</sub><sup>val</sup> | AP<sub>S</sub><sup>val</sup> | AP<sub>M</sub><sup>val</sup> | AP<sub>L</sub><sup>val</sup> | weights | | :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | | [**YOLOv4-CSP**](/cfg/yolov4_csp.cfg) | 640 | **49.1%** | **67.7%** | **53.8%** | **32.1%** | **54.4%** | **63.2%** | - | | [**YOLOR-CSP**](/cfg/yolor_csp.cfg) | 640 | **49.2%** | **67.6%** | **53.7%** | **32.9%** | **54.4%** | **63.0%** | [weights](https://github.com/WongKinYiu/yolor/releases/download/weights/yolor_csp.pt) | | [**YOLOR-CSP***](/cfg/yolor_csp.cfg) | 640 | **50.0%** | **68.7%** | **54.3%** | **34.2%** | **55.1%** | **64.3%** | [weights](https://github.com/WongKinYiu/yolor/releases/download/weights/yolor_csp_star.pt) | | | | | | | | | | [**YOLOv4-CSP-X**](/cfg/yolov4_csp_x.cfg) | 640 | **50.9%** | **69.3%** | **55.4%** | **35.3%** | **55.8%** | **64.8%** | - | | [**YOLOR-CSP-X**](/cfg/yolor_csp_x.cfg) | 640 | **51.1%** | **69.6%** | **55.7%** | **35.7%** | **56.0%** | **65.2%** | [weights](https://github.com/WongKinYiu/yolor/releases/download/weights/yolor_csp_x.pt) | | [**YOLOR-CSP-X***](/cfg/yolor_csp_x.cfg) | 640 | **51.5%** | **69.9%** | **56.1%** | **35.8%** | **56.8%** | **66.1%** | [weights](https://github.com/WongKinYiu/yolor/releases/download/weights/yolor_csp_x_star.pt) | | | | | | | | | Developing... | Model | Test Size | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | AP<sub>S</sub><sup>test</sup> | AP<sub>M</sub><sup>test</sup> | AP<sub>L</sub><sup>test</sup> | | :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | | **YOLOR-CSP** | 640 | **51.1%** | **69.6%** | **55.7%** | **31.7%** | **55.3%** | **64.7%** | | **YOLOR-CSP-X** | 640 | **53.0%** | **71.4%** | **57.9%** | **33.7%** | **57.1%** | **66.8%** | Train from scratch for 300 epochs... | Model | Info | Test Size | AP | | :-- | :-- | :-: | :-: | | **YOLOR-CSP** | [evolution](https://github.com/ultralytics/yolov3/issues/392) | 640 | **48.0%** | | **YOLOR-CSP** | [strategy](https://openaccess.thecvf.com/content/ICCV2021W/LPCV/html/Wang_Exploring_the_Power_of_Lightweight_YOLOv4_ICCVW
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Kin-Yiu, Wong · IIS, Academia Sinica · Taiwan
75
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:568b019fedea6ae0, topic:pytorch, readme:train from scratch
matched fp:568b019fedea6ae0, topic:yolo, readme:object detection