I recently convert my model to tensorflow lite but I only got the .tflite file and not a labels.txt for my Android project. So is it possible to create my own labels.txt using the classes that I used to classify? If not, then how to generate labels.txt?
You should be able to generate and use your own labels.txt. The file needs to include the label names in the order you provided them in training, with one name per line.
Run the following code after installing the TFLite Model Maker library and pass the dataset for classification:
data = ImageClassifierDataLoader.from_folder('folder/')
train_data, test_data = data.split(0.8)
model = image_classifier.create(train_data)
loss, accuracy = model.evaluate(test_data)
model.export('image_classifier.tflite', 'imageLabels.txt')
On running it in Colab or locally, the labels files would be auto-generated with the categories from each of the subfolders.
Related
I am trying to use the Open Images dataset to train a binary CNN model (Orange vs. Not Orange).
I use the OID v4 toolkit to download images of few classes both in train and test.
Now I'm stuck with how to conert the multiclass shape in each directory to a binary.
I believe I need some tool to change the subfolders (=classes) name.
I've succeeded using os and shutil packages to manipulate the directories as requested.
TXS.
I have been searching for a method to do this for so long, and I can not find an answer. Most threads I found are of people wanting to do the opposite.
Backstory:
I am experimenting with some pre-trained models provided by the tensorflow/models repository. The models are saved as .pb frozen graphs. I want to fine-tune some of these models by changing the final layers to suit my application.
Hence, I want to load the models inside a jupyter notebook as a normal keras h5 model.
How can I do that?
do you have a better way to do so?
Thanks.
seems like all you would have to do is download the model files and store them in a directory. Call the directory for example c:\models. Then load the model.
model = tf.keras.models.load_model(r'c:\models')
model.summary() # prints out the model layers
# generate code to modify the model as you typically do for transfer learning
# compile the changed model
# train the model
# save the trained model as a .h5 file
dir=r'path to the directory you want to save the model to'
model_identifier= 'abcd.h5' # for abcd use whatever identification you want
save_path=os.path.join(dir, model_identifier)
model.save(save_path)
I need to train tensorflow deeplab model with my shoes dataset. Then i will use this model in order to remove background of image shoe. How could i train it ? Could you explain step by step ? You have any example for this situation ?
tensorflow/deeplab
You will need read some parts of Deeplab code
Download repo
Now you need to put your data in tfrecord in proper format
Use some of scripts in https://github.com/tensorflow/models/tree/master/research/deeplab/datasets to download and generate example datasets
Prepare analogous script for your shoes dataset
Add information about data to Deeplab source file https://github.com/tensorflow/models/blob/master/research/deeplab/datasets/data_generator.py add info in analogous format like example datasets
Check flags for architecture https://github.com/tensorflow/models/blob/master/research/deeplab/common.py
Check specific flags and then train, export, count statistics or visualize using train.py, vis.py, export_model.py, eval.py in folder https://github.com/tensorflow/models/tree/master/research/deeplab
I am using matterport repository to train MASK RCNN on a custom dataset. I have been successful in training. Now I want to save the trained model and use it in a web application to detect objects. How do I save the mask rcnn model after training? Please guide me.
The link of the repository:
https://github.com/matterport/Mask_RCNN
Based on this discussion on GitHub, it appears that trained model or weights of matterport/Mask RCNN can be saved as a JSON file in a manner similar to those trained via standard Keras:
import keras
import json
def save_model(trained_model, out_fname="model.json"):
jsonObj = trained_model.keras_model.to_json()
with open(out_fname, "w") as fh:
fj.write(jsonObj)
save_model(model, "mymodel.json")
Update: If you run into the error related to thread-like object, you might find this post helpful...
In the Inspect_model.ipynb notebook under the "Load Model" topic you can save it after it loads the model in inference mode.
in the folder Mask_RCNN/logs generates a folder inside it
I am not sure if we really need to save the whole model again since normally when we used the matterport git we just train new weights on the existing architecture and doesnt make changes to the architecture. When we used this for a pet project , post training - we defined a new model as the MASK RCNN object (from mrcnn.model import MaskRCNN) with the parameter mode as inference and then loaded the newly trained weights model.load_weights('<logpath/trainedweights.h5>', by_name=True)
Tensorflow recently seems to have released pretrained model for instance segmentation using MaskRCNN as per below tweet.
https://twitter.com/TensorFlow/status/963472849511092225
I downloaded mask_rcnn_resnet101_atrous_coco_2018_01_28.tar.gz from this and was trying to figure out how to use it.I found frozen model (pb) file and loaded the graph in Tensorboard.
But I cant figure out what should be the input for the model.I couldn't find a node where simply I can input an image , though I was able to locate nodes where we get classes,masks,bounding boxes etc.
Also there seems to be no details online on how to use it (May be because it is new)
If you follow this tensorflow tutorial it will show you how to run the frozen model on a single/group of images. To apply this to the model you downloaded, the simplest way would be to first replace the line:
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
with a path to your downloaded model i.e.
PATH_TO_CKPT = '/absolute/path/to/frozen_inference_graph.pb'
Then there is no need to run the code under the section Download Model. The rest should work the same.