I have downloaded the .weights and .cfg file for YOLOv3 from darknet (link: https://pjreddie.com/darknet/yolo/) I want to create a model and assign the weights from these files, and I want to save the model with the assigned weights to a .h5 file so that I can load the .h5 model into Keras by using keras.models.load_model().
Please help.
You should check the instructions given in this repository. This is basically the keras implementation of YOLOv3 (Tensorflow backend).
Download YOLOv3 weights from YOLO website.
Convert the Darknet YOLO model to a Keras model.
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
As you have already downloaded the weights and configuration file, you can skip the first step. Download the convert.py script from repository and simply run the above command.
Note: Above command assumes that yolov3.cfg, yolov3.weights and model_data(folder) are present at the same path as convert.py.
For people getting error from this try changing the layers part in 'convert.py'
Not sure if it was version problem but changing the way converter.py file was loading 'keras.layers' solved all errors for me
Related
Is this SavedModel just for Tensorflow front-end applications or it can be use to reload model in keras format. I created it using tf.saved_model.save and now I don't know what to make of it.
Following the guide above I was able to load a SavedModel directory, and it seemingly no use, not trainable nor use to predict input like model.predict, and that the only thing I have since I lost the h5 file in my files **cough trashbin **cough.
Note: I noticed this guide tell me to use tf.keras.models.load_model('inceptionv3')
and it return this
error
You have saved the model using tf.saved_model.save and so the correct way to load it back is tf.saved_model.load('inceptionv3'). This is also suggested in your error image.
After loading model, you can try doing prediction as follows:
model = tf.saved_model.load('inceptionv3')
out = model(inputs)
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 followed the tutorial at
https://machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/
After successful training I got 5 .h5 files:
mask_rcnn_kangaroo_cfg_0001.h5
mask_rcnn_kangaroo_cfg_0002.h5
mask_rcnn_kangaroo_cfg_0003.h5
mask_rcnn_kangaroo_cfg_0004.h5
mask_rcnn_kangaroo_cfg_0005.h5
I am a newbie to this, so my understanding may be wrong:
How can I convert these .h5 files to .pb files or better to .tflite files, so I can use them in an Android Object Detection app?
You don't need to convert these .h5 to .pb, you can directly convert keras .h5 files to tflite. Here is the official documentation on how to.
Make sure to have the model with layers supported by TFLite, as mentioned here.
Once you have the .tflite model you can run an interpreter on Android.
I am working on tensorflow project. I have successfully train, test and make prediction using python flask. But in order to make prediction each time i have to again load full model using checkpoints. If I save the model .h5 format, I don't need to load the dataset to predict the datasets. I am don't know how to save the tensorflow model in .h5 format using checkpoints. If anybody know how to do, please help me or forward me any link if possible.
Thanks.
You can save and restore tensorflow models using tf.train.Saver class.
Although this doesn't store the models in .h5 format.
You can refer to these sections for further clarity:
Save
Restore
Hope this helps!
Went thru https://tensorflow.github.io/serving/serving_basic and was able to run inference MNIST example using Tensorflow Serving server.
Now, I would like to use a trained modified InceptionV3 model (which generates 2 files: .pb and .txt) to export and use it for inference.
Serving Basic tutorial use mnist_saved_model.py for training and exporting the model. Is this file to be modified for the trained modified InceptionV3 model? Also, what is the difference between mnist_saved_model.py and mnist_export.py?
Looked at How to serve the Tensorflow graph file (output_graph.pb) via Tensorflow serving? but the example mnist_saved_model.py creates a directory called 1 and subdirectories as shown below
$> ls /tmp/mnist_model/1
saved_model.pb variables
Thoughts??