I've run the following command:. But the program terminates with the following message: Illegal instruction core dumped. I honestly don't understand why, because I've downloaded the model using the model downloader that comes with OpenVINO, running:. Are you using OpenVino R1 the latest and greatest version? If not please download it and try it. Any idea about what could be causing my problem? Indeed that is very strange.

A command similar to the below works perfectly of course on Windows not Linux. Your issue has to do with the python interpreter in your environment. It's not related to OpenVino. Remember Python is itself istp and infj "c" program - so if a core dump happens when you use Python, there is something going on with your version of Python3.

All the model optimizer code are pure python scripts also. Running the command, however, resulted in the same error: Illegal instruction core dumped. Looks like there's a problem with TensorFlow models. I'm pretty confident that there's a problem with the version of TensorFlow that is installed on my machine namely version 1.

Just to be thorough, I've tried running:. Here's the error log:. Partia lInfer. Glad you solved your issue. Yes I knew it was not an OpenVino problem when you said "core dump" using mo tools. Because those prerequisites scripts would have ensured that you've got the correct version of Tensorflow. However 3 for number of channels is OK. So you need to pass in a valid set of 4 positive numbers into the mo.

So as you can see from my command above I did not run into this error. Model Optimizer does not accept negative values for batch, height, width and channel number.

In any case, I get the following output, which is honestly overwhelming:. Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary. I don't know what to do at this point, guess I'll have to give up if you don't have further suggestions.

I totally understand how Model Optimizer errors can be overwhelming! Thank you Shubha, the link you provided was extremely helpful. I've tried your command and, surprisingly, it finally worked!This will use many of the techniques that were shown throughout the book, such as:. An object detector can find the locations of several different types of objects in the image.

The detections are described by bounding boxes, and for each bounding box the model also predicts a class. There are many variations of SSD. Another common model architecture is YOLO. Like SSD it was designed to run in real-time. There are many architectural differences between them, but in the end both models make predictions on a fixed-size grid.

Each cell in this grid is responsible for detecting objects in a particular location in the original input image. What matters is that they take an image as input and produce a tensor, or multi-array as Core ML calls it, of a certain size as output. This tensor contains the bounding box predictions in one form or another. For an in-depth explanation of how these kinds of models work and how they are trained, see my blog post One-shot object detection. The number of bounding boxes per cell is 3 for the largest grid and 6 for the others, giving a total of boxes.

These models always predict the same number of bounding boxes, even if there is no object at a particular location in the image. To filter out the useless predictions, a post-processing step called non-maximum suppression or NMS is necessary. In order to turn the predictions into true rectangles, they must be decoded first. Until recently, the decoding and NMS post-processing steps had to be performed afterwards in Swift. The model would output an MLMultiArray containing the grid of predictions and you had to loop through the cells and perform these calculations yourself.

But as of iOS 12 and macOS You simply perform a Vision request on the image and the result is an array of VNRecognizedObjectObservation objects that contain the coordinates and class labels for the bounding boxes. Vision automatically decodes the predictions for you and even performs NMS.

How convenient is that! You can download it here. Note: The following instructions were tested with coremltools 2. The part of the TensorFlow graph that we keep has one input for the image and two outputs: one for the bounding box coordinate predictions and one for the classes. TensorFlow models can be quite complicated, so it usually takes a bit of searching to find the nodes you need. Another trick is to simply print out a list of all the operations in the graph and look for ones that seem reasonably named, then run the graph up to that point and see what sort of results you get.

Interestingly, they use a different output node.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. To train we used Ubuntu We created the tflite model using these scripts:. What this app basically does, it takes a pre-recorded video, decodes it frame-by-frame using FFmpegMediaMetadataRetriever and passes the bitmap into tflite to detect objects there.

The app is built with gradle and we are using 'org. We scale the bitmap down to x and convert it from ARGB into 3 float channels and call tflite like this:. As you can see the NPE occurs deep in the libtensorflow and we are basically running out of ideas what we could do to fix it, so any help is appreciated.

It happens on both a physical device and the android sandbox API We used this as a starting point and also the Tensorflow tflite demo form the tensorflow repository. We managed to successfully without NullPointer call tflite using a tflite graph generated by the following command:.

ssdlite mobilenet v2 coco

Learn more. Asked 1 year, 1 month ago. Active 1 year ago. Viewed times. We scale the bitmap down to x and convert it from ARGB into 3 float channels and call tflite like this: Log.

It happens on both a physical device and the android sandbox API 28 We used this as a starting point and also the Tensorflow tflite demo form the tensorflow repository.

MobileNet v2

Guido Forrestor Guido Forrestor 39 3 3 bronze badges. Active Oldest Votes. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. Podcast Programming tutorials can be a real drag. Featured on Meta. Community and Moderator guidelines for escalating issues via new response….

Feedback on Q2 Community Roadmap. Triage needs to be fixed urgently, and users need to be notified upon…. Technical site integration observational experiment live on Stack Overflow.Efficient networks optimized for speed and memory, with residual blocks. All pre-trained models expect input images normalized in the same way, i. The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input.

MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer.

Additionally, non-linearities in the narrow layers were removed in order to maintain representational power. To analyze traffic and optimize your experience, we serve cookies on this site.

MobilenetV2 SSDLite train finetune

By clicking or navigating, you agree to allow our usage of cookies. Learn more, including about available controls: Cookies Policy. MobileNet v2 By Pytorch Team. Efficient networks optimized for speed and memory, with residual blocks View on Github Open on Google Colab. Compose [ transforms.

Resizetransforms. CenterCroptransforms. ToTensortransforms. To get probabilities, you can run a softmax on it. Tutorials Get in-depth tutorials for beginners and advanced developers View Tutorials. Resources Find development resources and get your questions answered View Resources.The world cup season is here and off to an interesting start. Who ever thought the reining champions Germany would be eliminated in the group stage :.

For the data scientist within you lets use this opportunity to do some analysis on soccer clips.

重磅!MobileNet-YOLOv3来了(含三种框架开源代码)

With the use of deep learning and opencv we can extract interesting insights from video clips. And all of this can be done real time. You can find the code I used on my Github repo. Tensorflow Object Detection API is a very powerful source for quickly building object detection models. COCO dataset is a set of 90 commonly found objects.

See image below of objects that are part of COCO dataset. In this case we care about classes — persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing. If you are new to OpenCV please see the tutorial below:.

OpenCV Tutorial. OpenCV allows us to identify masks of specific colours and we can use that to identify red players and yellow players. See example below of how OpenCV masking works to detect red colour in the image.

Now lets go into the code in detail. And install all the dependencies using these instructions. The main steps I followed are please follow along in the jupyter notebook on my Github :.

So now you can see how simple combination of deep learning and OpenCV can produce interesting results. Now that you have this data, there are many ways to draw additional insights from it:. You can try those as well. I have my own deep learning consultancy and love to work on interesting problems. I have helped many startups deploy innovative AI based solutions.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Does this model learn anything at all? Learn more. Ask Question. Asked 1 year, 9 months ago.

Active 1 year, 9 months ago. Viewed times. Farshad Farshad 11 1 1 silver badge 6 6 bronze badges. Active Oldest Votes. I determined the number of classes and path of tfrecords as well. Also i reduced the batch size to 6 because of low memory.

I think i should select a bigger batch size in ssd model in order that ssd reduce the loss properly. Sign up or log in Sign up using Google.

Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. Podcast Programming tutorials can be a real drag. Featured on Meta. Community and Moderator guidelines for escalating issues via new response…. Feedback on Q2 Community Roadmap. Triage needs to be fixed urgently, and users need to be notified upon….

Technical site integration observational experiment live on Stack Overflow. Dark Mode Beta - help us root out low-contrast and un-converted bits. Related 0. Hot Network Questions. Question feed. Stack Overflow works best with JavaScript enabled.My problem is that when I use the converted model for detection, all I get is a DetectionOuput with shape [1,1,7] that consists of only zeros, except the first element which is I get the same result for different example images.

I have tried 2 different models, both with the same result. Since they are from the supported model zoo, I think the base models are not the problem. There is probably something wrong with the mo.

Any pointers would be appreciated. Does it produce proper output? Yes, the python demo produces proper output and while taking a look at the demo code, I found my error. In my own code, I did the resizing and reshaping, but not the transposing. After adding the line that changes the layout to CHW, the object detection is working now! Quote: Shubha R. Intel wrote:. When should I add this option?

ssdlite mobilenet v2 coco

It seems that the model works well although I did not do that. Skip to main content. Only nodes performing mean value subtraction and scaling if applicable are kept. And this is a minimal code sample that reproduces the problem: import cv2 import numpy as np from openvino.

ssdlite mobilenet v2 coco

Last post. RSS Top. Log in to post comments. Shubha R. Thanks, Shubha. Hello Shubha R. Thank you, mred. Thanks for using OpenVino! Leave a Comment Please sign in to add a comment. Not a member? Join today. For more complete information about compiler optimizations, see our Optimization Notice.

EdgeTPU object detection - SSD MobileNet V2

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