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@@ -2,7 +2,7 @@
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| Model | Scale | Batch | AP<sup>test<br>0.5:0.95 | AP<sup>test<br>0.5 | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
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|----------|-------|-------|-------------------------|--------------------|------------------------|-------------------|-------------------|--------------------|--------|
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-| RTCDet-N | 640 | 8xb16 | | | 36.6 | 54.6 | 8.2 | 2.1 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/rtcdet_n_coco.pth) |
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+| RTCDet-N | 640 | 8xb16 | 36.8 | 54.9 | 36.6 | 54.6 | 8.2 | 2.1 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/rtcdet_n_coco.pth) |
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| RTCDet-T | 640 | 8xb16 | 41.9 | 60.6 | 41.6 | 60.1 | 17.9 | 4.7 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/rtcdet_t_coco.pth) |
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| RTCDet-S | 640 | 8xb16 | 45.2 | 64.0 | 44.7 | 63.7 | 31.5 | 8.4 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/rtcdet_s_coco.pth) |
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| RTCDet-M | 640 | 8xb16 | | | | | | | |
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@@ -11,9 +11,9 @@
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| Model | Scale | Batch | AP<sup>test<br>0.5:0.95 | AP<sup>test<br>0.5 | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
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|----------|-------|-------|-------------------------|--------------------|------------------------|-------------------|-------------------|--------------------|--------|
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-| RTCDet-P | 320 | 8xb16 | | | | | | | |
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-| RTCDet-P | 416 | 8xb16 | | | | | | | |
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-| RTCDet-P | 512 | 8xb16 | | | | | | | |
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+| RTCDet-P | 320 | 8xb16 | | | | | 1.00 | 1.07 | |
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+| RTCDet-P | 416 | 8xb16 | | | | | 1.68 | 1.07 | |
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+| RTCDet-P | 512 | 8xb16 | | | | | 2.55 | 1.07 | |
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| RTCDet-P | 640 | 8xb16 | | | | | 4.05 | 1.07 | |
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- For training, we train my RTCDet series series with 300 epochs on COCO.
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