你好！问一下目标检测网络R-FCN的问题 about caffe-model HOT 8 OPEN

如果你用的是xx-merge.prototxt的结构，就要用xx-merge.caffemodel来finetune
其他的话看看均值方差，测试脚本有没有问题

from caffe-model.

目前我并没有自己生成预训练模型xx-merge.caffemodel，是直接用hub主的cls中百度网盘的resnext50-32x4d.caffemodel，所以基础网络prototxt文件就相应用了deploy_resnext50-32x4d.prototxt，当然进行了相应的修改，将前几层网络设为不参入学习，并没有xx-merge.caffemodel。
至于均值方差的话，我的caffe版本直接用的是caffe_window，而R-FCN用的是py-R-FCN-master，并没有hub主的py-RFCN-priv-master和caffe-priv，应该不涉及需要修改均值与方差吧

from caffe-model.

py-R-FCN-master是不支持除方差操作的，而resnext的均值和方差跟resnet还是不同，这是个问题，但是应该不会导致这么大的精度衰减，其他的就不清楚了

from caffe-model.

嗯嗯，谢了，关键是我训练时精度参数全程并不收敛，上下浮动，而且浮动幅度很大
I0226 23:27:26.994686 7644 solver.cpp:2quancheng44] Train net output #0: accuarcy = 0.296875
I0226 23:27:26.994693 7644 solver.cpp:244] Train net output #1: loss_bbox = 0.0694371 (* 1 = 0.0694371 loss)
I0226 23:27:26.994698 7644 solver.cpp:244] Train net output #2: loss_cls = 2.26333 (* 1 = 2.26333 loss)
I0226 23:27:26.994701 7644 solver.cpp:244] Train net output #3: rpn_cls_loss = 0.179019 (* 1 = 0.179019 loss)
I0226 23:27:26.994704 7644 solver.cpp:244] Train net output #4: rpn_loss_bbox = 0.191131 (* 1 = 0.191131 loss)
I0226 23:27:26.994709 7644 sgd_solver.cpp:106] Iteration 720, lr = 0.001
I0226 23:27:32.879098 7644 solver.cpp:228] Iteration 740, loss = 2.51227
I0226 23:27:32.879128 7644 solver.cpp:244] Train net output #0: accuarcy = 0.609375
I0226 23:27:32.879135 7644 solver.cpp:244] Train net output #1: loss_bbox = 0.0947561 (* 1 = 0.0947561 loss)
I0226 23:27:32.879139 7644 solver.cpp:244] Train net output #2: loss_cls = 1.35206 (* 1 = 1.35206 loss)
I0226 23:27:32.879142 7644 solver.cpp:244] Train net output #3: rpn_cls_loss = 0.168547 (* 1 = 0.168547 loss)
I0226 23:27:32.879146 7644 solver.cpp:244] Train net output #4: rpn_loss_bbox = 0.0914559 (* 1 = 0.0914559 loss)
I0226 23:27:32.879150 7644 sgd_solver.cpp:106] Iteration 740, lr = 0.001
I0226 23:27:40.231842 7644 solver.cpp:228] Iteration 760, loss = 1.9404
I0226 23:27:40.231873 7644 solver.cpp:244] Train net output #0: accuarcy = 0.398438
I0226 23:27:40.231879 7644 solver.cpp:244] Train net output #1: loss_bbox = 0.168068 (* 1 = 0.168068 loss)
I0226 23:27:40.231883 7644 solver.cpp:244] Train net output #2: loss_cls = 1.87442 (* 1 = 1.87442 loss)
I0226 23:27:40.231886 7644 solver.cpp:244] Train net output #3: rpn_cls_loss = 0.0348021 (* 1 = 0.0348021 loss)
I0226 23:27:40.231890 7644 solver.cpp:244] Train net output #4: rpn_loss_bbox = 0.0939938 (* 1 = 0.0939938 loss)
I0226 23:27:40.231894 7644 sgd_solver.cpp:106] Iteration 760, lr = 0.001
I0226 23:27:46.188066 7644 solver.cpp:228] Iteration 780, loss = 2.85662
I0226 23:27:46.188096 7644 solver.cpp:244] Train net output #0: accuarcy = 0.328125
I0226 23:27:46.188102 7644 solver.cpp:244] Train net output #1: loss_bbox = 0.36219 (* 1 = 0.36219 loss)
I0226 23:27:46.188107 7644 solver.cpp:244] Train net output #2: loss_cls = 2.1627 (* 1 = 2.1627 loss)
I0226 23:27:46.188110 7644 solver.cpp:244] Train net output #3: rpn_cls_loss = 0.0994308 (* 1 = 0.0994308 loss)
I0226 23:27:46.188113 7644 solver.cpp:244] Train net output #4: rpn_loss_bbox = 0.259526 (* 1 = 0.259526 loss)
I0226 23:27:46.188117 7644 sgd_solver.cpp:106] Iteration 780, lr = 0.001
speed: 0.312s / iter
I0226 23:27:52.118432 7644 solver.cpp:228] Iteration 800, loss = 3.19266
I0226 23:27:52.118461 7644 solver.cpp:244] Train net output #0: accuarcy = 0
I0226 23:27:52.118468 7644 solver.cpp:244] Train net output #1: loss_bbox = 0.111914 (* 1 = 0.111914 loss)
I0226 23:27:52.118472 7644 solver.cpp:244] Train net output #2: loss_cls = 3.05117 (* 1 = 3.05117 loss)
I0226 23:27:52.118476 7644 solver.cpp:244] Train net output #3: rpn_cls_loss = 0.125618 (* 1 = 0.125618 loss)
I0226 23:27:52.118479 7644 solver.cpp:244] Train net output #4: rpn_loss_bbox = 0.123091 (* 1 = 0.123091 loss)
I0226 23:27:52.118484 7644 sgd_solver.cpp:106] Iteration 800, lr = 0.001
I0226 23:27:58.066153 7644 solver.cpp:228] Iteration 820, loss = 3.22972
I0226 23:27:58.066182 7644 solver.cpp:244] Train net output #0: accuarcy = 0
I0226 23:27:58.066190 7644 solver.cpp:244] Train net output #1: loss_bbox = 0.291357 (* 1 = 0.291357 loss)
I0226 23:27:58.066195 7644 solver.cpp:244] Train net output #2: loss_cls = 2.98972 (* 1 = 2.98972 loss)
I0226 23:27:58.066197 7644 solver.cpp:244] Train net output #3: rpn_cls_loss = 0.149604 (* 1 = 0.149604 loss)
I0226 23:27:58.066201 7644 solver.cpp:244] Train net output #4: rpn_loss_bbox = 0.269975 (* 1 = 0.269975 loss)

from caffe-model.

@hp-93 你好，我也遇到跟你同样的问题了，请问这个问题你现在解决了么？我用的是https://github.com/YuwenXiong/py-R-FCN这个链接的版本，请问你也是用的这个版本么？另外我发现，不仅仅是把基础网络修改为resnext50训练不收敛，我还尝试过改成resnet18，mobilenet-V1，mobilenet-V2，resnext26，还有其他第三方在resnet50上修改网络，全部都不收敛，似乎只有resnet50的结构才收敛，并且在原有的resnet50结构上任意删减层数，几乎都收敛（我甚至删减到12层，也收敛）。我现在完全找不到问题所在。请问你现在有什么进展么？谢谢！

from caffe-model.

@firefox1031 我的caffe models的图像预处理都有除方差操作，而https://github.com/YuwenXiong/py-R-FCN是不支持的，可能是这个问题

from caffe-model.

@soeaver 谢谢你的回复，我接下来用你的版本尝试一下。另外还有个问题，你的模型里面提到了把batchnorm层合并到scale层里面，请问具体是怎么做的？

from caffe-model.

@soeaver 你好，我尝试了你的版本，仍然有loss不收敛的问题，我是用的自己的数据集，总共三类，我在rfcn_voc_resnet18-priv-merge-ohem.prototxt里面做了相应的类别修改，使用的预训练模型是https://github.com/HolmesShuan/ResNet-18-Caffemodel-on-ImageNet中的预训练模型，cfg文件设置如下：
EXP_DIR: rfcn_end2end
TRAIN:
HAS_RPN: True
IMS_PER_BATCH: 1
BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True
RPN_POSITIVE_OVERLAP: 0.7
RPN_BATCHSIZE: 256
PROPOSAL_METHOD: gt
BG_THRESH_LO: 0.1
BATCH_SIZE: 128
AGNOSTIC: False
SNAPSHOT_ITERS: 10000
RPN_PRE_NMS_TOP_N: 6000
RPN_POST_NMS_TOP_N: 300
TEST:
HAS_RPN: True
AGNOSTIC: False
训练过程中也会出现loss的剧烈跳动，简要结果如下：
Iteration 31880，lr =0.0001，loss=0.89979，accuracy=0.90411
Iteration 31900，lr =0.0001，loss=2.11952，accuracy=0
Iteration 31920，lr =0.0001，loss=0.97738，accuracy=0.769231
Iteration 31940，lr =0.0001，loss=1.89307，accuracy=0

时不时就会出现这样的剧烈跳动，请问为什么会有这样的问题？

from caffe-model.