I was exploring turicreate ObjectDetector API. The documentation mentions it is a trained model. I wanted to know if I can use this trained model & detect the 1000 labels which was used to originally train this turi model. All the examples mention to train with our dataset, I do not want to train but wanted to use pre-trained model which can classify. Any help is appreciated.
Is your question about how to load and use a pre-trained model? Turi create API docs mentions a load_model method:
model.save('my_model_file')
loaded_model = tc.load_model('my_model_file')
EDIT: Yep, ObjectDetector exposes a save method that works well with load_model.
Related
I trained an Bert-based NER model using Pytorch framework by referring the below article.
https://www.depends-on-the-definition.com/named-entity-recognition-with-bert/.
After training the model using this approach, I saved that model using torch.save() method. Now, I want to retrain the model with new dataset.
Can someone please help me on how to perform retraining/transfer learning as I'm new to NLP and transformers.
Thanks in advance.
First, you can read the doc on pytorch loading, the model that can be very helpful to retrain the save model in a new dataset. I will provide the example of loading a save model. That can be very helpful to you
Original Doc :- https://pytorch.org/tutorials/beginner/saving_loading_models.html
Example code :- https://pythonguides.com/pytorch-load-model/
This two link are very helpful to train the new dataset on save model
I am trying to implement TFLite model for food detection and segmentation. This is the model i chose suitable for my food images dataset: [https://tfhub.dev/s?deployment-format=lite&q=inception%20resnet%20v2].
I searched over google to understand how the images are required to be annotated, but only end up in confusion. I understand the dataset is converted to TFRecords and then fed to the pretrained model. But for training the model with custom dataset, does not it require an annotation file? I dont see any info about this on TF hub either.
Please can anyone help me on this!
The answer to your question is depends on what model do you plan to train.
In the case of a model for food detection and segmentation you do need annotations when training. If you do not provide the model with labeled training data as it is a supervised learning model it cannot learn from them.
If you were to train an autoencoder the data does not need to be annotated. Hope the keywords used in this answer help you out search for more information about the topic.
I have an implementation of YOLOv3 using mobilenet working (based on https://github.com/Adamdad/keras-YOLOv3-mobilenet). However, I already use mobilenet for feature extraction on other functionalities. I would like to reuse these features extracted from the images to the other models. How can I get them from YOLOv3 model already trained? Is it possible to get the vectors from an intermediate layer of the model?
I've tried this with no success: https://keras.io/getting_started/faq/#how-can-i-obtain-the-output-of-an-intermediate-layer-feature-extraction
Thanks
I trained an h5 model using Keras and i don't know how to use it on other programs for other images. And also this question is not duplicate or i couldn't find any solution for that. Thanks...
Use models from keras library as below.
from keras open models
model = models.load_model("model.h5")
When training deep semantic segmentation models, it is often convenient to visualize a sample of predictions on the validation set during the training. Right now I'm simply saving some predictions to disk on my training server. I'm looking to migrate this task to TensorBoard. Simply put, I wan't to visualize a set of predictions (say 5) over each epoch.
I know there is a simple way to do it in pure TensorFlow like tf.summary.image(..) but I don't see any easy way to incorporate this into the Keras TensorBoard callback.
Any guidance would be much appreciated.
Fabio Perez provided an answer which should do exactly what you're looking for here How to display custom images in TensorBoard using Keras?