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@@ -9,12 +9,12 @@
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| RTCDet-L | 640 | 8xb16 | | | | | | | |
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| RTCDet-X | 640 | | | | | | | | |
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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 | | | | | 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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+| Model | Scale | Batch | 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 | 25.4 | 40.1 | 1.00 | 1.07 | - |
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+| RTCDet-P | 416 | 8xb16 | 29.4 | 45.8 | 1.68 | 1.07 | - |
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+| RTCDet-P | 512 | 8xb16 | 32.0 | 49.2 | 2.55 | 1.07 | - |
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+| RTCDet-P | 640 | 8xb16 | 33.8 | 51.7 | 4.05 | 1.07 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/rtcdet_p_coco.pth) |
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- For training, we train my RTCDet series series with 300 epochs on COCO.
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- For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation, following the setting of [YOLOX](https://github.com/ultralytics/yolov5), but we remove the rotation transformation which is used in YOLOX's strong augmentation.
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yjh0410