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idata
Community Manager
460 Views

Help required in using a retrained model for custom object detection on a Raspberry Pi 3 + Movidius

Hello.

 

Firstly, let me start by saying that I'm not educated in this field at all. I have tried learning a lot, mostly for fun, but considering I have difficulties with reading, it's quite hard to find resources on how this kind of things works, I've found. I am a software developer, full time, so figured a bit of python or whatever would serve me well and away we would go. How wrong I was.

 

A couple of weeks ago I gave up on trying to use my custom trained model (UK registration plate detection). It worked really quite well on PC using a python script by following this tutorial (Faster-RCNN-Inception-V2-COCO). I have also followed this tutorial (ssd_mobilenet_v1_coco), which is aimed at training in the cloud. Using both tutorials, I was able to successfully use about 600 images to create my model. I've tried registering to the Intel seminar things, but I never get a response to those, so I am unable to get any value from those.

 

The last time I looked at it (about the beginning of this month) I had hit a brick wall (after spending many hours every day for about 10 days) and ended up spitting my dummy out. Along with that, I decided the best thing for it was to get some fresh eyes on it and give it about a week as it actually started to consume me a little bit…

 

Anyway, my question is, if it is possible at all, how would I go about taking my retrained models and using them on the Movidius?

 

My 'requirements'/details are as follows:

 

1) My use case does not have to use Tensorflow – it would be a bonus if it did as I already have 2x working models, but if another format such as Tiny Yolo needs to be trained, I'm quite happy to do that again.

 

2) Ideally, I will be able to just send it an image of some kind, rather than a camera feed

 

3) To have a response time that is pretty reasonable – I'm wanting to make myself a small API that uses this, so a decent response time would be amazing.

 

Whether it be a link to a valuable literature (aforementioned poor reading skills make long articles a real chore to filter through to makes sure they're of value) or a good video, or better yet - the answer, that would be fantastic and massively appreciated as it's actually getting me really down that I've not been able to crack this.

 

Many many thanks,

 

Luke
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10 Replies
idata
Community Manager
65 Views

Have you gone through the basic examples in the NCAppZoo first?

 

https://developer.movidius.com/start

 

https://developer.movidius.com/docs

 

https://developer.movidius.com/examples

 

As far as using your own graph, you let me know when you figure that out.

 

I used retrain.py to reuse the inception v3 graph, created a pb file and could not compile it.

 

Trying again to figure this out. I had to go back to using OpenCV for a quick fall back.
idata
Community Manager
65 Views

@chicagobob123 Thanks. I will either look at those or refresh if I have this weekend. I will let you know how it goes.

idata
Community Manager
65 Views

The NCAppZoo shows how to load a Tensorflow graph that has been COMPILED.

 

Once you have working compiled graph the rest is pretty straight forward.

 

You load the graph send it images and get the inference back. I suggest you look at the Security Cam example.

 

It breaks it down the into the basic steps.

 

Check for the Movidius device,

 

Open the device,

 

Load the graph,

 

Init your camera or get a still image to compare etc..

 

Get an image,

 

do an inference

 

and decide what to do with the results.

 

There is code to all of that but its actually pretty simple and easy to do.
idata
Community Manager
65 Views

Dear all,

 

Im having issues running the already existing graph generated in ncappzoo/caffe/SSD_MobileNet Im using the live-object-detector.py py which is part of the NCAPPZOO without changes.

 

Error message is:

 

pi@raspberrypi:~/ncsdk/ncappzoo/apps/live-object-detector $ make run

 

Running live-object-detector.py

 

python3 live-object-detector.py --video 0

 

Traceback (most recent call last):

 

File "live-object-detector.py", line 211, in

 

open( ARGS.labels ) if line != 'classes\n']

 

FileNotFoundError: [Errno 2] No such file or directory: '../../caffe/SSD_MobileNet/labels.txt'

 

Makefile:16: recipe for target 'run' failed

 

make: *** [run] Error 1

 

This labels file was not generated when I compiled the graph, I`ve checked the github documentation for SSD_mobilenet but there is nothing about this file there,, so does anybody have any idea where to get this file?

 

Thanks in advance!
idata
Community Manager
65 Views

Did you do a makeall when you installed AppZoo? it typically fixes those issues.

 

As far as whats in the labels.txt file just some labels.

 

do a makeall here. I think that should generate what you need.

 

workspace/ncappzoo/caffe

idata
Community Manager
65 Views

Thanks for the support @chicagobob123 , unfortunatelly everything is already there, please see the result of make all below:

 

pi@raspberrypi:~/ncsdk/ncappzoo/caffe/SSD_MobileNet $ make all

 

making prereqs

 

making prototxt

 

Prototxt file already exists, skipping download.

 

making caffemodel

 

caffemodel already exists, skipping download.

 

making compile

 

NCS graph file already exists, skipping compile.

 

It seems this file labels.txt is generated by oder process or has to be downloaded.

 

Any comment is welcome!

 

Regards,
idata
Community Manager
65 Views

I have found the content of the labels.txt in github:

 

0: background

 

1: aeroplane

 

2: bicycle

 

3: bird

 

4: boat

 

5: bottle

 

6: bus

 

7: car

 

8: cat

 

9: chair

 

10: cow

 

11: diningtable

 

12: dog

 

13: horse

 

14: motorbike

 

15: person

 

16: pottedplant

 

17: sheep

 

18: sofa

 

19: train

 

20: tvmonitor
idata
Community Manager
65 Views

how did you not have it if it was on github? Glad you have it now

idata
Community Manager
65 Views

Good question, it seems it was lost in one previous version.

 

Please find the link where I got it below:

 

https://github.com/movidius/ncappzoo/commit/14e06acbe266e862afe70fef3a0381df1a11e1cc
idata
Community Manager
65 Views

Dear Intel colleagues, do you have a guideline how to retrain the MobileNet SSD for a custom object detection as requested in the first post?

 

Thanks in advance!