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@@ -58,12 +58,12 @@ For example:
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python train.py --cuda -d coco --root path/to/COCO -v yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale
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```
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-| Model | Scale | IP | AP<sup>val<br>0.5:0.95 | AP<sup>test<br>0.5:0.95 | FPS<sup>3090<br>FP32-bs1 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
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-|--------|-------|------|------------------------|-------------------------|--------------------------|-------------------|--------------------|--------|
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-| YOLOv1 | 640 | √ | 35.5 | | 100 | 9.0 | 2.3 | |
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-| YOLOv2 | 640 | √ | | | | 33.5 | 8.3 | |
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-| YOLOv3 | 640 | √ | | | | 86.7 | 23.0 | |
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-| YOLOv4 | 640 | √ | | | | 175.4 | 46.5 | |
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+| Model | Scale | IP | Epoch | AP<sup>val<br>0.5:0.95 | AP<sup>test<br>0.5:0.95 | FPS<sup>3090<br>FP32-bs1 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
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+|--------|-------|------|-------|------------------------|-------------------------|--------------------------|-------------------|--------------------|--------|
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+| YOLOv1 | 640 | √ | 150 | 35.5 | | 100 | 9.0 | 2.3 | |
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+| YOLOv2 | 640 | √ | 150 | | | | 33.5 | 8.3 | |
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+| YOLOv3 | 640 | √ | 150 | | | | 86.7 | 23.0 | |
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+| YOLOv4 | 640 | √ | 150 | | | | 175.4 | 46.5 | |
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*All models are trained with ImageNet pretrained weight (IP). All FLOPs are measured with a 640x640 image size on COCO val2017. The FPS is measured with batch size 1 on 3090 GPU from the model inference to the NMS operation.*
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