I'm working on a line detection project using deep learning. What are the benefits of using an image dataset in a pascal format?
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Dear google mediapipe team
Could you offer the quantized models related pose, face, iris and hand of mediapipe's tflie file
I have used mediapipe's holistic at android with qualcomm device.
I want to improve the performance by using qualcomm's snpe sdk.
the sdk requires quantized models.
if you can offer quantized models of holistic, my plan is that I am going to try to replace tflite releated code to dlc code.
dlc(dinamic layer container) is snpe's format to do inference at qualcomm dsp, and sdk provide converting tool for quantized tflite file.
thanks,
Hoyeon
I have developed body gesture usign mediapipe.
but it's out-throuput couldn't meet our specs.
if I can use snpe sdk, I will achieve my mission.
I checked stackflow and tensorflow pages how I can convert tflite file to quantized tflite file and I found it required saved model
I'm kinda new to TensorFlow and Keras, so please excuse any accidental stupidity, but I have an issue. I've been trying to load in models from the TensorFlow Detection Zoo, but haven't had much success.
I can't figure out how to read these saved_model folders (they contain a saved_model.pb file, and an assets and variables folder), so that they're accepted by Keras. Nor can I figure out a way to convert these models so that they may be loaded in. I've tried converting the SavedModel to ONNX, and then convert the ONNX-model to Keras, but that didn't work. Trying to load the original model as a saved_model, and then trying to to save this loaded model in another format gave me no success either.
Since you are new to Tensorflow (and I guess deep learning) I would suggest you stick with the API because the detection zoo models best interface with the object detection API. If you have already downloaded the model, you just need to export it using the exporter_main_v2.py script. This article explains it very well link.
I have come across googles new BodyPix TensorflowJS model today, and I want to get it running on Android and iOS, but using TensorFlow Lite and CoreML. I was wondering if someone could point me towards the best way to convert this model into TensorFlow Lite. I have done conversions between TensorFlow Lite and CoreML so that's no problem.
I've read a few documents on how to do this, but im a little confused as those documents mention a model.json file or something similar, which the BodyPix src directory contains a multitude of files, which im unsure on what any of them do.
https://github.com/tensorflow/tfjs-models/tree/master/body-pix
Any help or pointers would be appreciated.
I'm looking at a DLC file which represents the graph used for a neural network inside of the Snapdragon Neural Processing Engine.
https://developer.qualcomm.com/docs/snpe/model_conv_tensorflow.html
I would like to visualize this model in something like tensorboard. My understanding is tensorboard requires PB file which is used by tensorflow to save graphs.
Is there any way to convert a DLC file to a Tensorflow PB for visualization or another way to achieve this aim?
NPE SDK does not provide tool to convert a DLC file to PB/Any other framework supported model.
A platform like Tensorboard, which helps in debug and visualization of the model created are not available from NPE SDK.
I want to convert a Tensorflow model with the following structure to a .mlmodel file for use in an iOS app:
cub_image_experiment/
logdir/
val_summaries/
test_summaries/
finetune/
val_summaries/
cmds.txt
config_train.yaml
config_test.yaml
I'm following this tutorial: https://github.com/visipedia/tf_classification/wiki/CUB-200-Image-Classification
However, I'm having trouble understanding the structure of the project. Which files are important and how do I convert all the separate config files and everything into a single .mlmodel file so that I can use in my application?
I've looked online and all I could find was how to convert .caffemodel to .mlmodel or .pb file to .mlmodel. These are all single files, however my project has multiple files. I found a tutorial on how to convert a tf model into a single .pb file, however, that model's structure was different and it did not contain any yaml files. My project is not focused on creating a model at the moment, but merely integrating a model into an iOS app. I found this model interesting for an app idea and wanted to know if it can be integrated. If there are any tutorials out there that might help me in this sort of problem please let me know.
None of that stuff is used by the Core ML model. The yaml files etc are used only to train the TF model.
All you need to provide is a frozen graph (a .pb file) and then convert it to an mlmodel using tfcoreml.
It looks like your project doesn't have a frozen graph but checkpoints. There is a TF utility that you can use to convert the checkpoint to a frozen graph, see https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py