hi,
Very incredible paper. Really cool to see the ability to pick up the small clouds and cloud shadows compared to Maja & Fmask.
I'd like to test applying the L2A UNet model in my existing Tensorflow-based image segmentation pipeline. I currently use something like the following to save my model weights
saver.restore(sess,tf.train.latest_checkpoint(meta_path))
output_node_names = ['conv2d_13/Sigmoid']
# Freeze the graph
frozen_graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def,
output_node_names)
with open('../../models/620-240-apr/predict_graph.pb', 'wb') as f:
f.write(frozen_graph_def.SerializeToString())"
and then something like the following to load them:
predict_file = tf.io.gfile.GFile(args.predict_model_path + "predict_graph.pb", 'rb')
predict_graph_def.ParseFromString(predict_file.read())
predict_graph = tf.import_graph_def(predict_graph_def, name='predict')
Do you know if it would be possible to make something like this out of then .hdf5 file for the U-Net weights? do you know the input, output variable names? This is the only way I know to have multiple TF graphs in memory at once -- I have one for doing model predictions, one for super resolving 20m band to 10m, and would have a third graph with your model weights
hi,
Very incredible paper. Really cool to see the ability to pick up the small clouds and cloud shadows compared to Maja & Fmask.
I'd like to test applying the L2A UNet model in my existing Tensorflow-based image segmentation pipeline. I currently use something like the following to save my model weights
and then something like the following to load them:
Do you know if it would be possible to make something like this out of then .hdf5 file for the U-Net weights? do you know the input, output variable names? This is the only way I know to have multiple TF graphs in memory at once -- I have one for doing model predictions, one for super resolving 20m band to 10m, and would have a third graph with your model weights