• LDoubleZhi

    是否直接修改yaml里的backbone_pretrained: 'checkpoints/mobilenet_v2.pth'就可以只当模型文件,再args.do_test=True就可以了?

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  • LDoubleZhi

    如题,只有coco的训练代码

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  • LDoubleZhi

    貌似是burn_in期间学习率太大导致nan,但是原论文burnin也不小啊

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  • LDoubleZhi

    [Epoch 0/150][Iter 0/86][lr 0.000000][Loss: anchor 9.16, iou 8.31, l1 35.46, conf 99672.05, cls 16.66, imgsize 608, time: 26.08]
    [Epoch 0/150][Iter 10/86][lr 0.000000][Loss: anchor 18.68, iou 18.18, l1 101.71, conf 2369.89, cls 39.56, imgsize 608, time: 39.37]
    [Epoch 0/150][Iter 20/86][lr 0.000003][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 416, time: 45.88]
    [Epoch 0/150][Iter 30/86][lr 0.000015][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 352, time: 26.93]
    [Epoch 0/150][Iter 40/86][lr 0.000047][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 384, time: 24.05]
    [Epoch 0/150][Iter 50/86][lr 0.000114][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 480, time: 30.52]
    [Epoch 0/150][Iter 60/86][lr 0.000237][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 576, time: 39.93]
    [Epoch 0/150][Iter 70/86][lr 0.000439][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 480, time: 32.17]
    [Epoch 0/150][Iter 80/86][lr 0.000749][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 448, time: 36.69]
    [Epoch 1/150][Iter 0/86][lr 0.001000][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 352, time: 23.47]
    [Epoch 1/150][Iter 10/86][lr 0.001000][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 352, time: 15.24]
    [Epoch 1/150][Iter 20/86][lr 0.001000][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 512, time: 34.18]
    [Epoch 1/150][Iter 30/86][lr 0.001000][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 544, time: 48.47]
    [Epoch 1/150][Iter 40/86][lr 0.001000][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 416, time: 28.83]
    [Epoch 1/150][Iter 50/86][lr 0.001000][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 416, time: 19.87]
    [Epoch 1/150][Iter 60/86][lr 0.001000][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 352, time: 18.90]
    [Epoch 1/150][Iter 70/86][lr 0.001000][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 576, time: 19.08]
    [Epoch 1/150][Iter 80/86][lr 0.001000][Loss: anchor nan, iou nan, l1 nan, conf nan, cls nan, imgsize 608, time: 34.87]

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  • LDoubleZhi

    如题,asff中的3个融合权重参数是怎么监督学习

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  • LDoubleZhi

    @金天 大佬,c++TRT貌似有内存泄露?

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  • LDoubleZhi

    @金天 我目前用的是101,50的需要自己写onnx导出吗?

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  • LDoubleZhi

    float16推理速度在135ms,我直接在ctdetConfig.h设置input_size报错,必须要设置的和训练时一样吗?

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  • LDoubleZhi

    @金天 我好像找到原因了,demo_det_r010_custom.py里heads = {'hm': 5, 'reg': 2, 'wh': 2}中的hm要根据自己的类设置,哭了,搞了一周

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