dog-qiuqiu / mobilenet-yolo Goto Github PK

请问下大佬， 如何训练 Ultra lightweight 106 point face-landmark model ？
非常感谢。

大大，可不可说明一下如何训练及使用，对于新手来说很有必要，谢谢！！

想尝试魔改下模型，重新训练，但不清楚yoloface的数据集是哪个

你好，想了解一下yolov4或者yolov4-tiny目前能在mnn推理框架中使用吗，有没有推理时间的测试数据啊

大佬，有什么建议将yolov-4转为caffe吗，现在卡到了通道数减半这个操作上，caffe有哪个对应层可以对应吗，或者几个层组合实现这个功能，非常感谢

请问一下我们我的电脑运行yolov4-tiny 能跑80fps，但是MobileNetV2-YOLOv3-Lite只能跑11fps，这是怎么回事呢？

armv8.2变快啦～～

模型是YOLO fastest的模型，转换完caffe模型后，使用detector.py进行测试的时候，报错KeyError: 'layer79-conv'，这个地方需要修改key吗

因为CUDA Version: 11.0，所以装的cudnn为 v8.0.4，设置cuda=1，cudnn=1，然后编译，然后报错了，如下
error:CUDNN_CONVOLUTION_FWD_PREFER_FASTEST undeclared (first use in this function); did you mean CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3? int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST; ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3 compilation terminated due to -Wfatal-errors.

网上有找原因，可能是cudnn版本过高导致，只能cudnnv3-v7版本，但是对应cuda11.0的cudnn只有v8版本的

然后同样的配置编译AlexeyAB/darknet的版本能够通过，但我想做mobilenet版本的yolo

which dataset is trained for MobileNetV2--Lite.conv.57 ?
COCO? or VOC?

請問能提供 MNN 的 sample code嗎？

Hi.
Very interested to yoloface-500k model.
if input image size is 352*228, which size of face I can detect at lease.
Please notice me smallest face size could be detected in yoloface-500k-v2.
Thanks.

感谢分享,想了解下weights是如何转换到mnn model，有参考的链接分享下嚒，是weights->onnx->mnn流程嚒?

Hello,

Thanks for the great repo.
I was interested in the app so I downloaded & ran on my android device, and the app was closed right after I opened it.
I downloaded via link below.
https://github.com/dog-qiuqiu/Android_MobileNetV2-YOLOV3-Nano-NCNN/blob/master/app/release/MobileNetv2-yolov3-nano.apk

is there any limitation or options for running app?

Thanks a lot.

caffemodel or darknet model ,How to convert yoloface-500k model to tensorrt

貌似yolov3-nano训练出的模型caffe输出正常，但是mnn输出就是乱的，我对比了两个模型每一层输出，发现是在layer72-upsample层开始不一样，感觉是
#layer {
# bottom: "layer71-route"
# top: "layer72-upsample"
# name: "layer72-upsample"
# type: "Upsample"
# upsample_param {
# scale: 2
# }
#}
layer {
bottom: "layer71-route"
top: "layer72-upsample"
name: "layer72-upsample"
type: "Interp"
interp_param {
height:20 #upsample h size
width:20 #upsample w size
}
}
模型转换上有点瑕疵，请问大大怎么修改才能解决？谢谢！！

README

@dog-qiuqiu Hi! Thank you for your work. Please clarify why batch is so small (16) comparing to default yolov3 (64):
https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/blob/2cda47a036e0378cb6c988326c3b53016eeb2b4f/MobileNetV2-YOLOv3-SPP/MobileNetV2-YOLOv3-SPP.cfg#L2

在手机上验证了这个效果相当不错，想把它移植到海思的nnie上。我自己训练出来的模型，经过ruyi 转换没有啥异常，放到板子上跑的时候检测结果不准确，或者说完全错误，没有一个目标对得上。不知道那里除了问题，网上查了一圈有说图片预处理不对，我目前转模型的预处理就是 图片 RGB，还有一个就是 data_scale 1/256 ，不知还有没有什么其他的地方没弄对？？ @dog-qiuqiu 希望能指点一下，谢谢！！！！

请问下，MobilenetV2-Yolov3-lite如何转换到ncnn下?yolo-fastest如何转化?我训练的数据要么转化成功加载的时候直接崩溃，要么就是无法正常转换。

你好，如果要用MobileNetV2-YOLOv3-Lite训练自己的数据集，直接用你提供的cfg和weight文件和MobileNetV2--Lite.conv.57，在darknet下就可以了吗？

Hello!
I've met a bug when I use your resnext152-32x4d.cfg file. My project was forked from ultralytics/yolov3.
Bug description: The size of feature maps are unmatched to do 'shortcut' op.
Where: The config file of ResneXt152 backbone - resnext152-32x4d.cfg.

The code as follow is one part of the cfg file and it's shown that feature maps in sample level X/2 cannot ADD with sample level X.

[shortcut] # -4 (sample level X)
...
[convolutional]# -3
...
[convolutional]# -2 (Downsample)
groups = 32
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]# -1 (input of the shortcut)
...
[shortcut]# 0 (sample level X/2)
from=-4
activation=leaky

python darknet2caffe.py MobileNetV2-YOLOv3-Nano-voc.cfg MobileNetV2-YOLOv3-Nano-voc.weights MobileNetV2-YOLOv3-Nano-voc.prototxt MobileNetV2-YOLOv3-Nano-voc.caffemodel

我想问问VOC的平均精度是用的VOC2007的测试集，那训练集是VOC07+12还是同时在COCO上也训练了呀？谢谢

OS : ubuntu18.04

Follow the instructions, install and build the caffe, able to convert nano, lite, the error messages are

Traceback (most recent call last):
  File "darknet2caffe.py", line 443, in <module>
    darknet2caffe(cfgfile, weightfile, protofile, caffemodel)
  File "darknet2caffe.py", line 8, in darknet2caffe
    net_info = cfg2prototxt(cfgfile)
  File "darknet2caffe.py", line 309, in cfg2prototxt
    bottom2 = topnames[prev_layer_id2]
KeyError: 126

I hope to train yoloface-500k model on custom database.
How to train this model?
Where can I get trainer for this model.

使用了最新的NCNN，但项目里面MobileNetV2-YOLOv3-Nano-voc模型还是无法转化，报错：

(py35) qglobal@qglobal-ThinkStation-P328:~/ncnn/build/tools/caffe$ ./caffe2ncnn MobileNetV2-YOLOv3-Nano-voc.prototxt MobileNetV2-YOLOv3-Nano-voc.caffemodel
[libprotobuf ERROR google/protobuf/text_format.cc:274] Error parsing text-format caffe.NetParameter: 2066:20: Message type "caffe.LayerParameter" has no field named "upsample_param".
read_proto_from_text failed

大佬，你这个表里模型测试的NCNN模型是咋转化的