some assets about ncnn
ncnn-assets's Introduction
ncnn-assets's People
Forkers
ritchesc wshlin schumann2016 mansenaa xysui wangzl2011 ztianlin johnrayn wuliajie jack-xf gsyn77 woo00w jasonliu03 raneee zombie0117 cqray1990 sejudyblues suyuan945 githubolive kqhuynguyen yippeesoft wzjai2018 lhu1994 excalibur-ssr sunjunlishi robin990 kuyu linhandai yingunjun yc-huang mingx9527 hanshan123 shenmayufei ncnnnnn facerecognitionorg zengwb-lx gillbam thp430426 tsyjwct zsharp7 wuzujiong dl19940602 wangryan7720 charyun fliperworld rogerluojie caishanli liuxinshi hiter-lwp cookingthe1st gitleej a243845305 liyuan90 wblksheep kanwangshijie123 xiaoyu1004 benieq huangwgang clveryang asr-engineering-consulting yinjiayang yyqgood ai-mzq reddevil1310 yuluohuatan bl6g6 joonhow liguiyuan fenlai chengquan cuongtvee kerry678231 maxin19940317 bxkdstar123 rufle xiaopeng487364 liandaniel geoffzhang supercool1 yumendecc vtn98 pqmsoft1 repeerc jedibobo nixondutt killerdamon dpc-xjtu wangrui996 cydia2018 anhuipl2010 crisjiang zhaoyan07 tainguyen2000 hukai97 alsj213 tigercc8 jackwanglei2020 wdrabbit duhuasong jinji-lncnn-assets's Issues
onnx模型转换成ncnn
用官方模型转换为onnx,然后再转换成param和bin,模型也能转换成功,但是报错。想知道你们是如何转换的,报错如下:
Shape not supported yet!
Shape not supported yet!
Shape not supported yet!
Unsupported unsqueeze axes !
Unsupported unsqueeze axes !
Unsupported unsqueeze axes !
Unknown data type 0
Shape not supported yet!
Shape not supported yet!
Shape not supported yet!
Unsupported unsqueeze axes !
Unsupported unsqueeze axes !
Unsupported unsqueeze axes !
Unknown data type 0
Shape not supported yet!
Shape not supported yet!
Shape not supported yet!
Unsupported unsqueeze axes !
Unsupported unsqueeze axes !
Unsupported unsqueeze axes !
Unknown data type 0
Convert yolov5s_6.0 to ncnn format?
Hi @nihui ,
Thanks for your assets. Could you please guide me on how to convert yolov5s_6.0
to the ncnn
format? I have seen the converted output in your repo.
Thank you so much!
您好,请问这个项目具体是用哪个工具转换的模型呀?
您好, 可以分享一下具体的转换过程吗? 非常感谢!
mobilenetv2_yolov3 caffe2ncnn转出的模型不能运行
hello,,nihui大佬,我用github.com/eric612/MobileNet-YOLO/blob/master/models/mobilenetv2_voc/yolo_lite/yolov3_m2.prototxt
的caffe模型,通过caffe2ncnn
做转换,转换生成了param
和bin
,load_model的时候报错: layer load_model 3 failed
。
请问您在转化这个caffemodel的时候,做了一些什么预处理吗,我需要做什么改动才能转换成功呢?请指教,多谢~
(卷卷卷qwq)
onnx转bin,para
同样的tiny版本,为什么我生成的bin 比官方提供的文件大一倍
which tool did you use to convert the yolov5s.py to yolov5s.bin/param?
can you write the steps on how to convert yolov5s to the ncnn format?
I converted the torch model to onnx then using ncnn tools converted to bin/param but it did not work
caffe2ncnn:生成的.param文件没有data层的信息
@nihui 你好, 我在用ncnn编译生成的caffe2ncnn工具转caffe模型至ncnn模型时, 生成的.param文件没有这两行:
Input data 0 1 data 0=300 1=300 2=3
Split splitncnn_0 1 7 data data_splitncnn_0 data_splitncnn_1 data_splitncnn_2 data_splitncnn_3 data_splitncnn_4 data_splitncnn_5 data_splitncnn_6
这样也导致.param文件的第2行s数据”148 181“的splitncnn_blob_count数为148, 因为没有Input,所以少1层。
我手动修改了148-->149,文件能正常加载。
看了下caffe2ncnn.cpp源代码,还是不太清楚为啥? 麻烦有空解答一下, 谢谢!
龙芯2k1000测试yolox模型无法正常运行,yolov5可以运行
loongson@ls2k:~/Desktop/ncnn/build/examples$ ./yolov5 01.jpg
0 = 0.88773 at 613.13 309.97 129.40 x 293.63
0 = 0.85717 at 717.60 414.13 116.84 x 182.27
0 = 0.83715 at 310.91 331.21 109.08 x 272.36
0 = 0.80530 at 818.37 296.96 71.07 x 209.47
0 = 0.79004 at 874.96 290.36 127.99 x 308.92
2 = 0.77431 at 993.14 334.77 85.86 x 87.92
0 = 0.77314 at 461.77 333.91 74.34 x 262.11
2 = 0.77174 at 91.87 364.67 105.94 x 101.68
0 = 0.70869 at 0.40 366.16 75.08 x 122.26
2 = 0.67867 at 146.39 357.82 287.35 x 151.01
0 = 0.65766 at 739.59 315.20 67.53 x 137.53
2 = 0.63094 at 353.93 347.06 256.41 x 129.11
2 = 0.62434 at 48.99 362.04 66.74 x 61.99
3 = 0.38121 at 5.74 421.51 67.24 x 91.54
0 = 0.34597 at 795.92 336.61 31.88 x 90.78
imshow save image to image.png
waitKey stub
loongson@ls2k:~/Desktop/ncnn/build/examples$ ./yolox 01.jpg
浮点数例外
nihui,/model/文件夹下考虑把中间生成的onnx也放在里面不?
在转模型的时候感觉不太能确定输入输出,在调试的时候,参考了nihui转的mobilenetssd的内容,查看了下输出。突然就想到有没有中间生成的onnx文件,也能检验一下自己是不是onnx本身转对了。
Converting yolov7-tiny from pytorch to ncnn
I trained yolov7-tiny on a custom dataset, converted it to onnx using this example and then converted that onnx to ncnn using this.
pt to onnx conversion
python export.py --weights MY_MODEL.pt --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 416 416 --max-wh 416
onnx to ncnn conversion
onnx2ncnn MY_MODEL.onnx MY_MODEL.param MY_MODEL.bin
When I open up the MY_MODEL.param
with netron, I see that the value of w
parameter for the reshape
layer of all the outputs is some positive integer (e.g. 676
). And the detection does not work (does not show any of the class).
When I compared MY_MODEL.param
with your yolov7-tiny.param
, I see that the value of w
parameter for the reshape
layer of all the outputs is -1
. If I manually change this value to -1
in my own MY_MODEL.param
, my model works with proper detection.
Can you please explain how you created your yolov7-tiny
ncnn? What are the exact steps you did?
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.