I have a resnet model trained in caffe. I want to try that with show_and_tell instead of inception_v3 image model.
What is the best way to convert the model form caffe to tensorflow
model ?
How would I go about switching the inception model to resnet
?
I'm not sure about the "best" way, but there are some tools that seem to address this, like https://github.com/ethereon/caffe-tensorflow
Related
I train a model by tensorflow 2 to detecting vehicles, but I want to Convert tensorflow saved_model to frozen inference graph.
Can any one help?
It is not the recommended way to save your model and i would suggest you use saved model.
People around here can help if you explain why you want to use frozen graph specifically and saved model won't help.
If you still want to try freezing you can use this internal method to do so.
How do I find more info on how the ssd_mobilenet_v1 tflite model on TFHub was trained?
Was it trained in such a way that made it easy to convert it to tflite by avoiding certain ops not supported by tflite? Or was it trained normally, and then converted using the tflite converter with TF Select and the tips on this github issue?
Also, does anyone know if there's an equivalent mobilenet tflite model trained on OpenImagesV6? If not, what's the best starting point for training one?
I am not sure about about the exact origin of the model, but looks like it does have TFLite-compatible ops. From my experience, the best place to start for TFLite-compatible SSD models is with the TF2 Detection Zoo. You can convert any of the SSD models using these instructions.
To train your own model, you can follow these instructions that leverage Google Cloud.
I need to Load a pre-trained model into Keras without the Weight and bias. I
also just want to use the Architecture of the model alone for my training.
Example:
I want to load coco_mobilenet model pre-trained without Weights and bias.
Any suggestions would be appreciated.
net=keras.applications.MobileNet(weight=None)
net.summary()
view keras mobilenet api for detail
Can someone point me to the Resnet34 pre-trained model on image-net using tensorflow? I am not sure but TF-slim trained model are same or would there be difference?
You can use Keras ResNet(18,34,50,101,152) pre-trained models https://github.com/qubvel/classification_models
I want to implements a faster-rcnn model using distributed tensorflow, But I have difficult to load a pretrained vgg model,How to do it? thanks
The TensorFlow tutorial on retraining inception is a good start to read. Then try to reproduce what it does starting from an already trained vgg model.