I´m new here, so please be kind and teach me if I did not provide all the information you need :)
I would like to compare Edge TPU with other edge device such as Myriad. I would like to select one object detection model and one image segmentation model. Considering the following link which shows supported operations, I have noticed that yolov3 cannot be compiled for EdgeTPU because it includes LeakyRelu.
https://coral.withgoogle.com/docs/edgetpu/models-intro/
For image segmentation, I'd like to use Deeplab. But I'm still don't know if operations included in deeplab v3+, such as atrous convolution or feature pyramid network, are supported.
I'd appreciate if someone teach me what models are usable on edgeTPU. Are there any models of image segmentation?
Did you already found below?
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/quantize.md
"mobilenetv2_coco_voc_trainaug_8bit":
deeplabv3_mnv2_pascal_train_aug_8bit/frozen_inference_graph.pb
This model is possible to converting to TFLite FlatBuffer.
And also possible to compile for edgetpu with edgetpu_compiler.
Note. edgetpu_api environment had updated.
You can find it below.
https://coral.withgoogle.com/news/updates-07-2019/
Yes. There are prepackaged segmentation models and code examples how to use them.
Here they are https://coral.ai/models/
Please share if you know where to find something similar for Movidius based VPU devices.
Here you can find all supported layers for edgetpu: https://coral.ai/docs/edgetpu/models-intro/#supported-operations.
And for Conv2D it says "Must use the same dilation in x and y dimensions.". So implementing a version of deeplab v3+ is possible for the edgetpu.
Related
I want to hand write a framework to perform inference of a given neural network. The network is so complicated, so to make sure my implementation is correct, I need to know how exactly the inference process is done on device.
I tried to use torchviz to visualize the network, but what I got seems to be the back propagation compute graph, which is really hard to understand.
Then I tried to convert the pytorch model to ONNX format, following the instruction enter link description here, but when I tried to visualize it, it seems that the original layers of the model had been seperated into very small operators.
I just want to get the result like this
How can I get this? Thanks!
Have you tried saving the model with torch.save (https://pytorch.org/tutorials/beginner/saving_loading_models.html) and opening it with Netron? The last view you showed is a view of the Netron app.
You can try also the package torchview, which provides several features (useful especially for large models). For instance you can set the display depth (depth in nested hierarchy of moduls).
It is also based on forward prop
github repo
Disclaimer: I am the author of the package
Note: The accepted format for tool is pytorch model
I would like to use AllenNLP Interpret (code + demo) with a PyTorch classification model trained with HuggingFace (electra base discriminator). Yet, it is not obvious to me, how I can convert my model, and use it in a local allen-nlp demo server.
How should I proceed ?
Thanks in advance
If your task is binary classification, you can look at the BoolQ example in https://github.com/allenai/allennlp-models/blob/main/training_config/classification/boolq_roberta.jsonnet. You can change that configuration to use a different model (such as Electra).
We also just put some new documentation out for the Interpret functionality: https://guide.allennlp.org/interpret
To give you a more specific answer, I'll need to know some more details, like what the task is you're trying to solve, how you trained the original model, etc.
I am doing a deep learning report that specifically uses the tensorflow library to identify and target the subject, and I want to find the same image as the identifying image, what should I do?
I have a tutorial on identifying images similar to the CNN model but with RFCN (rfcn_resnet101_coco) I have not done it yet. May everyone help.
Thank you very much
I am working on a project for face recognition with photos taken by cameras. I should use a virtual machine with spark and deeplearning4j.
The problem is that I didn't find the suitable algorithm and code to use for creating the neural network.
What is the difference between VGG16, keras, dataVec? and when we should use those models?
All the things you asked can be found through just a google search still giving you links just to give you a direction
VGG16
Keras
I am learning to implement a hand gesture recognition project. For this, I have gone through several tutorials where they use color information, background subtraction, various object segmentation techniques.
However, one that I would like to use is a method using cascading classifiers however I dont have much understanding in this approach. I have read several text and papers and I understand its theory however, I still dont understand what are good images to train the cascading classifer on. Is it better to train it on natural color images or images with hand gestures processed with canny edge detection or some other way.
Also, is there any method that uses online training and testing methods similar to openTLD but where the steps are explained. The openCV documentation for 2.3-2.4.3 are incomplete with respect to the machine learning and object recognition and tracking except for the code available at: http://docs.opencv.org/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.html
I know this is a long question but I wanted to explain my problem thoroughly. It would help me to understand the concept better than just to use online code.
Sincere thanks in advance!
if you think about haar classifier, a good tutorial is here