Comments (3)
你好,我这边给的实验13的[email protected]结果,是只进行稀疏性训练后的结果,并没有进行剪枝,所以我用实验13去做后续试验的原因是,稀疏性训练之后的结果比较好,所以没有进行剪枝之后的效果对比的实验。
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稀疏性训练不是越多越好,越多之后,趋于0的权重变多,网络自然会变差,但是100可能没有达到你说的过拟合的状态,具体你可以看下分布图,然后第二个问题,finetuning时间长应该可以更大恢复精度,但是应该还是会有个极限的。
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收到,非常感谢!我再去做一些对比实验看看效果如何!
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Related Issues (13)
- coco-hand and coco basic training command difference HOT 3
- Facing issue to convert pruned model to tflite model
- error
- Prune YOLOv5 with head discarded.
- what is the reason to limit percent threshold?
- question about sparsity training gpu memory HOT 2
- When 20% pruning, one layer itself becomes zero, is this a problem? HOT 7
- YOLOv5m6,l6这些模型也可以用这个代码剪枝吗 HOT 1
- Can't get attribute 'DetectionModel' on <module 'models.yolo' from '/content/yolov5/yolov5_prune/models/yolo.py'> HOT 1
- yolo.py中parse_pruned_model 获取每层剩余通道数疑问
- 剪枝报错stride[1,1] HOT 1
- Pruned model not able to detect object in image HOT 1
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