I want to know analyze the CNN in NetScope CNN analyzer, but unfortunately it only accepts caffe prototxt files. So is there any way to convert the tensorflow frozen model to caffe model to generate a prototxt file?
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I trained a model using yolov5, Then exported it to TensorFlow saved_model format, the result was a yolo5s.pt file. As far as I know yolov5 uses PyTorch, I prefer TensorFlow. Now I want to build a model in TensorFlow using the saved_model file, how can I do it?
It will be preferable if the solution is in google colab, I didn't included my code because I don't have any ideas how to start.
I have trained cnn model that was originally wrote on pytorch and saved in .pt
How can I analyze this model with Innvestigate tool? The tool is based on TensorFlow.
I have a darknet yolov4 model that is converted to tflite file and trained by COCO dataset for object detection.
I want to train that for my traffic sign dataset. (GTSDB)
How should I do that for my tflite file?
I don't want to do that in darknet and then convert to tflite. I want to transfer learning directly from tflite file.
How to convert resnet50 network written in eager_execution mode into coreml?
Here is the tf resnet50 implementation:
https://github.com/tensorflow/tensorflow/blob/r1.13/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py
At this point, coremltools and tfcoreml don't handle eager mode. You'll need to convert your TF model to a "frozen" .pb graph file first, and then you can use tfcoreml to convert it to Core ML.
If you can't turn your model into a frozen graph file, you can't use the automated conversion tools... But even in that case, you can still create a Core ML model using NeuralNetworkBuilder (part of coremltools), but you'll have to do all of this by hand.
General question: is there tooling to convert from tflite format to any other format?
I'm trying to convert a keras model to a CoreML model, but I can't because the model uses a layer type unsupported by CoreML (Gaussian Noise). Converting the keras .h5 model to a .tflite is simple, removes the offending layer (which is only used in training anyway), and performs some other optimisations. But it doesn't seem possible to convert out of the resultant tflite to any other format. Coremltools doesn't support tflite. I thought I could probably load the model from tflite into a tensorflow session, save a .pb from there, and convert that to coreml using coremltools, but I can't see a way to load the tflite model into a tensorflow session. I saw the documentation linked to in this question, but that seems to use the tflite interpreter to read the tflite model, rather than a "true" Tensorflow session.