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@@ -10,7 +10,7 @@
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| Model | Batch | Scale | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight | Logs |
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| Model | Batch | Scale | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight | Logs |
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|----------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|--------|
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|----------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|--------|
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-| YOLOv3-S | 1xb16 | 640 | | | 25.2 | 7.3 | | |
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+| YOLOv3-S | 1xb16 | 640 | 31.3 | 49.2 | 25.2 | 7.3 | [ckpt](https://github.com/yjh0410/YOLO-Tutorial-v3/releases/download/yolo_tutorial_ckpt/yolov3_s_coco.pth) | [log](https://github.com/yjh0410/YOLO-Tutorial-v3/releases/download/yolo_tutorial_ckpt/YOLOv3-S-COCO.txt) |
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- For training, we train redesigned YOLOv3 with 300 epochs on COCO. We also use the gradient accumulation.
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- For training, we train redesigned YOLOv3 with 300 epochs on COCO. We also use the gradient accumulation.
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- For data augmentation, we use the RandomAffine, RandomHSV, Mosaic and Mixup augmentation.
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- For data augmentation, we use the RandomAffine, RandomHSV, Mosaic and Mixup augmentation.
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