tflite | Examples using TensorFlow Lite API to run inference on Coral devices | Machine Learning library
kandi X-RAY | tflite Summary
kandi X-RAY | tflite Summary
Examples using TensorFlow Lite API to run inference on Coral devices
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Top functions reviewed by kandi - BETA
- Get a list of bounding boxes
- Return the output tensor
- Apply a function to each bounding box
- Return a new BBox with the given coordinates
- Return the size of the input
- Returns the input details for the given key
- Set the input size
- Get the input tensor
- Compute the intersection of two BBox boxes
- Intersect two BBoxes
- Load labels from file
- Draw objects
- Create a Tflite Interpreter object
- Returns the size of the input image
tflite Key Features
tflite Examples and Code Snippets
Lines of Code : 105License : Non-SPDX (Apache License 2.0)
Copydef create_html(tflite_input, input_is_filepath=True): # pylint: disable=invalid-name
"""Returns html description with the given tflite model.
Args:
tflite_input: TFLite flatbuffer model path or model object.
input_is_filepath: Tells if def __init__(self,
model_file,
input_arrays=None,
input_shapes=None,
output_arrays=None,
custom_objects=None):
"""Constructor for TFLiteConverter.
Args:
model_f def convert_phase(component, subcomponent=SubComponent.UNSPECIFIED):
"""The decorator to identify converter component and subcomponent.
Args:
component: Converter component name.
subcomponent: Converter subcomponent name.
Returns:
Community Discussions
Trending Discussions on tflite
QUESTION
I am working on training an object detection model on Google Colab based on 1. After training the model, I want to test the model on new image as in this Test the TFLite model on your image. I am obtaining an error when running the code in Run object detection and show the detection results:
...ANSWER
Answered 2022-Apr-14 at 13:05# define local path to save image
TEMP_FILE = '/tmp/image.png'
# In a notebook, run bash command to download image locally from URL
!wget -q -O $TEMP_FILE $INPUT_IMAGE_URL
# Open the image (with Pillow library most likely)
im = Image.open(TEMP_FILE)
# Resize it to 512*512 pixels with the antialiasing resampling algorithm
im.thumbnail((512, 512), Image.ANTIALIAS)
# Save output image to local file
im.save(TEMP_FILE, 'PNG')
QUESTION
Hi stackoverflow community,
I am trying to get a project leveraging Tensorflow Lite Micro to run on my ESP32 using PlatformIO and the Arduino framework (not ESP-IDF). Basically, I followed the guide in this medium post https://towardsdatascience.com/tensorflow-meet-the-esp32-3ac36d7f32c7 and then included everything in my already existing ESP32 project.
My project was compiling fine prior to the integration of Tensorflow Lite Micro but since integrating it, I am getting the following compile errors which seem to be related to the Tensorflow framework itself. When I uncomment everything related to Tensorflow, it compiles fine. But just when only including the following header files, it breaks:
...ANSWER
Answered 2022-Apr-05 at 03:13I resolved this for now by switching from the Arduino framework to the ESP-IDF framework. With this, it works like a charm.
QUESTION
I want to use a generator to quantize a LSTM model.
Questions
I start with the question as this is quite a long post. I actually want to know if you have manged to quantize (int8) a LSTM model with post training quantization.
I tried it different TF versions but always bumped into an error. Below are some of my tries. Maybe you see an error I made or have a suggestion. Thanks
Working Part
The input is expected as (batch,1,45). Running inference with the un-quantized model runs fine. The model and csv can be found here:
csv file: https://mega.nz/file/5FciFDaR#Ev33Ij124vUmOF02jWLu0azxZs-Yahyp6PPGOqr8tok
modelfile: https://mega.nz/file/UAMgUBQA#oK-E0LjZ2YfShPlhHN3uKg8t7bALc2VAONpFirwbmys
ANSWER
Answered 2021-Sep-27 at 12:05If possible, you can try modifying your LSTM so that is can be converted to TFLite's fused LSTM operator. https://www.tensorflow.org/lite/convert/rnn It supports full-integer quantization for basic fused LSTM and UnidirectionalSequenceLSTM operators.
QUESTION
I was trying to convert BigGAN model in tensorflow-hub(.pb) to a TensorFlow Lite file (.tflite) using the following code:
...ANSWER
Answered 2022-Jan-28 at 10:38Using TF 2.x to convert a TF 1.x model to a TensorFlow Lite file is tricky. I would recommend running your code example on Google Colab and switching to TF 1.x:
QUESTION
I am working with quantized neural networks (need input image with pixels [0, 255]) for a while. For the ssd_mobilenet_v1.tflite model the following standartization parameter are given though https://tfhub.dev/tensorflow/lite-model/ssd_mobilenet_v1/1/metadata/2 :
ANSWER
Answered 2022-Jan-26 at 10:25I would say that each value in the tensor is normalized based on the mean and std leading to black pixels, which is completely normal behavior:
QUESTION
I'm fairly new to this so please excuse mylack of knowledge. I'm trying to make an ML app with kivy, which detects certain objects. The problem is that I cannot include tensorflow and keras in my code because kivy doesn't allow apk conversion with it. So I came across tensorflow lite, which can run on android, but when I looked at a python example for it, I found out that it includes tensorflow-
...ANSWER
Answered 2022-Jan-06 at 19:54QUESTION
Can someone explain each line of this code?
Like what is the purpose of imageMean, imageStd, threshold.
I can't really find the documentation of this
...ANSWER
Answered 2021-Nov-24 at 07:39When performing an image classification task, it's often useful to normalize image pixel values based on the dataset mean and standard deviation. More reasons on why we need to do can be found in this question: Why do we need to normalize the images before we put them into CNN?.
The imageMean is the mean pixel value of the image dataset to run on the model and imageStd is the standard deviation. The threshold value stands for the classification threshold, e.g. the probability value above the threshold can be indicated as "classified as class X" while the probability value below indicates "not classified as class X".
QUESTION
I am trying to run a TensorFlow-lite model on my App on a smartphone. First, I trained the model with numerical data using LSTM and build the model layer using TensorFlow.Keras. I used TensorFlow V2.x and saved the trained model on a server. After that, the model is downloaded to the internal memory of the smartphone by the App and loaded to the interpreter using "MappedByteBuffer". Until here everything is working correctly.
The problem is in the interpreter can not read and run the model. I also added the required dependencies on the build.gradle.
The conversion code to tflite model in python:
...ANSWER
Answered 2021-Nov-24 at 00:05Referring to one of the most recent TfLite android app examples might help: Model Personalization App. This demo app uses transfer learning model instead of LSTM, but the overall workflow should be similar.
As Farmaker mentioned in the comment, try using SNAPSHOT in the gradle dependency:
QUESTION
I converted the original u2net model weight file u2net.pth to tensorflow lite by following these instructructions, and it is converted successfully.
However I'm having trouble using it in android in tensrflow lite, I was not being able to add the image segmenter metadata to this model with tflite-support script, so I changed the model and returned only 1 output d0 (which is a combination of all i.e d1,d2,...,d7). Then metadata was added successfully and I was able to use the model, but its not giving any output and returning the same image .
So any help would be much appreciated, in letting me know where I messed up, and how can I use this use this u2net model properly in tensorflow lite with android, thanks in advance ..
...ANSWER
Answered 2021-Aug-29 at 07:31I will write a long answer here. Getting in touch with the github repo of U2Net it leaves you with the effort to examine the pre and post-processing steps so you can aply the same inside the android project.
First of all preprocessing:
In the u2net_test.py file you can see at this line that all the images are preprocessed with function ToTensorLab(flag=0). Navigating to this you see that with flag=0 the preprocessing is this:
QUESTION
I converted an existing SavedModel to TFLite:
...ANSWER
Answered 2021-Aug-06 at 13:36In the TensorFlow version 2.5, only the models, converted from the from_saved_model API, will have a signature.
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