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Some tricks to improve yolov5. #2
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At first, I tired yolov5-four-FPN.
And I begin to retrain my custom dataset.
yolov5-four-fpn:
Unfortunately, I only have a 2070, 8GB graphics card. My batch-size is both 2. Some results about four output v5-p5:
mAP 0.665, latency 38.5ms. yolov5-fpn:
mAP 0.728, latency 31.4ms |
@SpongeBab thanks for the ideas! As I mentioned previously YOLOv5-P6 models include outputs at 4 scales already: |
Yeah.I just want to test whether increasing the number of FPN layers can bring improvement without increasing the number of network layers. If I use the P6 model, it will not be surprising to see improvements, as the deeper the network is, the higher the mAP is generally.
mAP: 0.741(+0.013), latency 34.9ms (+3.5ms) Although batch-size is 3, different from before,but it prove the better mAP with p5-fourHead. |
@SpongeBab yes P6 models benefit 640 trainings also. YOLOv5l6 models trained at 640 produce 49.0 mAP vs YOLOv5l models trained at 640 at 48.2. |
Hello, @glenn-jocher . |
👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs. Access additional YOLOv5 🚀 resources:
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Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed! Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐! |
🚀 Feature
original issue: ultralytics#3993
Edit:A list of tricks( TBC ): What I want to do.
To modifiy yolov5-p5 to Four Head prediction.
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