I am following a codelab tutorial by Google for image recognition:
https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#3
However, in this case the tutorial is using MobileNet v1 for object detection. In fact, these env variables are set:
IMAGE_SIZE=224
ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"
But what if I would like to use MobileNet with SSD or SquezeNet for object detection? I guess ARCHITECTURE variable must change in something like
ARCHITECTURE="ssd_mobilenet_0.50_${IMAGE_SIZE}"
I can't find any helpful resource.
The tutorial you are following is using this retrain script which is an older version of the official tensorflow retrain script.
While you can only use either MobileNet or InceptionV3 by using the codelab script, you can follow the official documentation on image retraining to retrain using any model available on Tensorflow Hub.
UPDATE:
MobileNet and SqueezeNet are not suitable for object recognition, but only for image classification. Thus, SSDMobileNet is the possible way.
Related
I converted a pretrained model to TF-lite and would like to deploy to the edge device.
If we got new training data and would like to improve the pretrained model, is it possible to do on the edge device?
Ex. Is there any method to train the model and save to TF-lite(FlatBuffer) again on edge device?
Thanks for any inputs!
On-device training is not fully supported yet on TF Lite but you can refer to this blog post to see how it can be done.
https://blog.tensorflow.org/2019/12/example-on-device-model-personalization.html
The basic idea is:
Split your model to a base subgraph (e.g. feature extractor in an image classification model) and a trainable head.
Convert the base subgraph to TF Lite as normal. Convert the trainable head to TF Lite using the experimental tflite-transfer-convert tool.
Retrain the trainable head on-device as you wish.
I have trained my custom data-set using the Tensor Flow Object Detection API. I run my "prediction" script and it works fine on the GPU. Now , I want to convert the model to lite and run it on the Google Coral Edge TPU Board to detect my custom objects. I have gone through the documentation that Google Coral Board Website provides but I found it very confusing.
How to convert and run it on the Google Coral Edge TPU Board?
Thanks
Without reading the documentation, it will be very hard to continue. I'm not sure what your "prediction script" means, but I'm assuming that the script loaded a .pb tensorflow model, loaded some image data, and run inference on it to produce prediction results. That means you have a .pb tensorflow model at the "Frozen graph" stage of the following pipeline:
Image taken from coral.ai.
The next step would be to convert your .pb model to a "fully quantized .tflite model" using the post training quantization technique. The documentation to do that are given here. I also created a github gist, containing an example of Post Training Quantization here. Once you have produced the .tflite model, you'll need to compile the model via the edgetpu_compiler. Although everything you need to know about the edgetpu compiler is in that link, for your purpose, compiling a model is as simple as:
$ edgetpu_compiler your_model_name.tflite
Which will creates a your_model_name_edgetpu.tflite model that is compatible with the EdgeTPU. Now, if at this stage, instead of creating an edgetpu compatible model, you are getting some type of errors, then that means your model did not meets the requirements that are posted in the models-requirements section.
Once you have produced a compiled model, you can then deploy it on an edgetpu device. Currently are 2 main APIs that can be use to run inference with the model:
EdgeTPU API
python api
C++ api
tflite API
C++ api
python api
Ultimately, there are many demo examples to run inference on the model here.
The previous answer works with general classification models, but not with TF object detection API trained models.
You cannot do post-training quantization with TF Lite converter on TF object detection API models.
In order to run object detection models on EdgeTPU-s:
You must train the models in quantized aware training mode with this addition in model config:
graph_rewriter {
quantization {
delay: 48000
weight_bits: 8
activation_bits: 8
}
}
This might not work with all the models provided in the model-zoo, try a quantized model first.
After training, export the frozen graph with: object_detection/export_tflite_ssd_graph.py
Run tensorflow/lite/toco tool on the frozen graph to make it TFLite compatible
And finally run edgetpu_complier on the .tflite file
You can find more in-depth guide here:
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tensorflowlite.md
I have used Tensorflow Object Detection API suuccessfuly by using ssd_mobilenet_v1_coco_11_06_2017 model.
Now I need to use Inception v3 model instead of mobilenet model.
Question:
can I use it for Tensorflow Object Detection API and how can I change the config file and how to find it?
Simply you can refer ssd_mobilenet_v1_coco.config file. it is used to give configuration for the Tensorflow object detection API. you can edit it and give modifications as you want.
Basically I am trying to use google's pre trained Speaker-id model for speaker detection. But this being a TensorFlow Lite model, I can't use it on my Linux pc. For that, I am trying to find a converter back to its frozen graph model.
Any help on this converter or any direct way to use tensorflow Lite pretrained models on desktop itself, will be appreciated.
You can use the converter which generates tflite models to convert it back to a .pb file if that is what you're searching for.
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md
Are there any resnet implementations in tensorflow? I came across a few (e.g. https://github.com/ry/tensorflow-resnet, https://github.com/xuyuwei/resnet-tf) but these implementations have some bugs (e.g. see the Issues section on the respective github page). I am looking to train imagenet using resnet and looking for tensorflow implementations.
There are some (50/101/152) in tensorflow:models/slim.
The example notebook shows how to get a pre-trained inception running, res-net is probably no different.
I implemented a cifar10 version of ResNet with tensorflow. The validation errors of ResNet-32, ResNet-56 and ResNet-110 are 6.7%, 6.5% and 6.2% respectively. (You can modify the number of layers easily as hyper-parameters.)
I tried to be friendly with new ResNet fan and wrote everything straightforward. You can run the cifar10_train.py file directly without any downloads.
https://github.com/wenxinxu/resnet_in_tensorflow
I implemented Resnet by use of ronnie.ai and keras. Both of tool are great.
While ronnie is more easy from scratch.