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How to implement LogisticRegressionCv on flutter

i have LogisticRegressionCv model it's .pkl file and import data as images but i don't know how to get it on flutter please help me If you know how or if I must to convert my model to other file formats.
please help me.
Thank you for your help.

as you've trained your model in python and stored it in pkl file. One method is in your flutter background, call python3 predict_yourmodel.py your_model_params and after the run, it will give your the model result.
Another way is implement a logisticRegressionCv in Flutter as it is a simple model, and easily be implemented. you can store all your params and l1 or l2 etc super-params in a txt instead of pkl file for readility.

Can Yolo-V3 trained model be converted to TensorFlow model?

I have trained my model of doors in yolo-v3 but now I need it in TensorFlow-Lite. But, I am facing a problem, that is, if I want to train my model for tensorflow, I need annotation file in ".csv" or ".xml" but the ones I have are "*.txt". I did found a software to create annotation files manually from drawing rectangles in pictures but I can not do that for thousands of images due to time shortage.
Can anyone guide me how to handle such situation?
I have followed the following link but the resulted model did not work.
https://medium.com/analytics-vidhya/yolov3-to-tensorflow-lite-conversion-4602cec5c239

i think it will be good to train tensorflow implementation on your data , then converting tensrflow model to tflite should be easy
here is yolov3 in tf : https://github.com/YunYang1994/tensorflow-yolov3
then use official tensorflow codes to convert to tflite : https://www.tensorflow.org/lite/convert

Convert TFLite (TensorFlow) to MLModel (Apple)

I'm trying to convert TFLite Face Mesh model to MLModel (Apple).
TFLite model description:
https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view
TFLite actual .tflite file:
https://github.com/google/mediapipe/blob/master/mediapipe/models/face_landmark.tflite
Looking at CoreMLTools provided by Apple (https://coremltools.readme.io/docs/introductory-quickstart) seems like it's possible, but all the samples codes demonstrate conversation from Keras and not from TFLite (although it's clearly supported):
How does one convert TFLite model to MLModel model?

As far as I know, there is no direct conversion from TFLite to Core ML. Someone could create such a converter but apparently no one has.
Two options:
Do it yourself. There is a Python API to read the TFLite file (flatbuffers) and an API to write Core ML files (NeuralNetworkBuilder in coremltools). Go through the layers of the TFLite model one-by-one, and add them to the NeuralNetworkBuilder, then save as a .mlmodel file.
Let TFLite do this for you. When you use the CoreMLDelegate in TFLite, it actually performs the model conversion on-the-fly and saves a .mlmodel file (or the compiled version, .mlmodelc). Then it uses Core ML to run this model. You can write some code to load the model with TFLite using the CoreMLDelegate, then grab the .mlmodel file that this created from the app bundle and use that.

Create TFLite file with InceptionV3 and Food101

Im trying to create a TFLITE model to use on Android.
Im using InceptionV3Model with Food101 dataset.
Im new to this all ML world.
I found this code from TensorFlow that Im trying to use :
https://github.com/tensorflow/hub/blob/master/examples/image_retraining/retrain.py
I can't figure out where to add and what to add to convert the model into TFLite.
Please explain me about checkpoints and frozen graphs, and help to create TFLite model.
Thanks.

You could first export the model to saved_model by settting --saved_model_dir
and then using TFLiteConverter.from_saved_model.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/lite/TFLiteConverter#from_saved_model

What are the proper file formats for Freezy graph?

I am using Tensorflow and Inception v3 CNN.
I want to save the trained network using the freeze_graph.py
What I have done till now is saving the model in input_graph.pb during training, by using tf.write_graph, and my thoughts are that this should be the “input graph” in the FLAGS.
When it comes to the remaining fields, such as input saver, output nodes and initializer nodes, I`m not sure how to create the proper file formats?
Another question is how to use the created .meta file from the training?
I would really appreciate if you could take some time to help me.