I'm new on the GEE and I'm trying to make landcover and I'm getting error
classified image: Layer error: Classifier.cart: This classifier has been removed. For more information see: http://goo.gle/deprecated-classifiers
how to fix it?
link of the project
Related
Hereis the code below of the problem. this code is of tuner component. I am try to create a data pipeline but the mode is not getting valid input.
Pictures are attached of the error
I have used this project from Github: https://github.com/nicknochnack/TFODCourse
The project contains a model that can detect License Plate on a given Vehicle image. The Github repo also contains code for the conversion of model into Tensorflow Lite file.
I used that code to generate TFLite file.
And then, I followed this link: https://developers.google.com/codelabs/tflite-object-detection-android
Where I downloaded the sample Application of Object detection model and following the instructions, I copied my TFLite files into the Android Application.
Now, if I run the application and take a photo, it gives me this error,
/TaskJniUtils: Error getting native address of native library: task_vision_jni
java.lang.RuntimeException: Error occurred when initializing ObjectDetector: Input tensor has type kTfLiteFloat32: it requires specifying NormalizationOptions metadata to preprocess input images.
at org.tensorflow.lite.task.vision.detector.ObjectDetector
I understand that I have to add Metadata in my TFLite model. so, I searched about it and ended up on this link: https://www.tensorflow.org/lite/models/convert/metadata#model_with_metadata_format
But I didn't understand at all what exactly should I be doing. Can anyone please help me in pointing to the right direction that for my problem specifically, what exactly do I need to do?
I am running into the problem whenever I try to load custom trained NER model of spacy inside docker container.
Note:
I am using latest spacy version 3.0 and trained that NER model using CLI commands of spacy, first by converting Train data format into .spacy format
The error throws as following(You can check error in image as hyperlinked):
config validation error
My trained model file structure looks like this:
custom ner model structure
But while run that model without docker it works perfectly. What wrong I have done in this process. Plz help me to resolve the error.
Thank you in advance.
Observing KeyError:'CaseFoldUTF8' while loading the model when loading the model that I had fine-tuned using pre trained BERT from tf-hub
I got the same error. Adding import tensorflow_text at the top worked for me.
I am using facenet model.... When i am doing classifier training it shows this message, but image alignment process with this model is going good...
def load_model(model):
# Check if the model is a model directory (containing a metagraph and a checkpoint file)
# or if it is a protobuf file with a frozen graph
model_exp = os.path.expanduser(model)
if (os.path.isfile(model_exp)):
print('Model filename: %s' % model_exp)
with gfile.FastGFile(model_exp,'rb') as f:
graph_def = tf.GraphDef()
print("Graph def value: ",graph_def)
print(type(graph_def))
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
Can anyone help me to clear this issue?
And also the above code works well in local the issue occurs in heroku server
In the above code the print statement shows an op as,
Graph def value:
<class 'tensorflow.core.framework.graph_pb2.GraphDef'>
Below is a screenshot for an issue:
The error is due to the model serving support is not working on heroku... Better you need to use the paid account on heroku with machinelearning dependencies... Or you can go with some other online deployment which supports tensorflow model serve.