I am currently working with the Tensorflow Object-Detection API and I want to fine-tune a pre-trained model. Therefore, a hyperparameter-tuning is required.
Does the API already provide some kind of hyperparameter-tuning (like a grid search)? If there is nothing available, how can I implement a simple grid search to tune (the most relevant) hyperparameters?
Furthermore, does the API provide some kind of Early Stopping function that automatically aborts the training process if the accuracy does not change anymore.
Thanks a lot in advance.
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I'm looking into training an object detection network using Tensorflow, and I had a look at the TF2 Model Zoo. I noticed that there are noticeably less models there than in the directory /models/research/models/, including the MobileDet with SSDLite developed for the jetson xavier.
To clarify, the readme says that there is a MobileDet GPU with SSDLite, and that the model and checkpoints trained on COCO are provided, yet I couldn't find them anywhere in the repo.
How is one supposed to use those models?
I already have a custom-trained MobileDetv3 for image classification, and I was hoping to see a way to turn the network into an object detection network, in accordance with the MobileDetv3 paper. If this is not straightforward, training one network from scratch could be ok too, I just need to know where to even start from.
If you plan to use the object detection API, you can't use your existing model. You have to choose from a list of models here for v2 and here for v1
The documentation is very well maintained and the steps to train or validate or run inference (test) on custom data is very well explained here by the TensorFlow team. The link is meant for TensorFlow version v2. However, if you wish to use v1, the process is fairly similar and there are numerous blogs/videos explaining how to go about it
I am using Tensorflow Federated to train a text classification model with the federated learning approach.
Is there any way to apply Early Stopping on the client-side? Is there an option for cross-validation in the API?
The only thing I was able to find is the evaluation:
evaluation = tff.learning.build_federated_evaluation(model_fn)
Which is applied to the model by the end of a federated training round.
Am I missing something?
One straightforward way to control the number of steps a client takes when using tff.learning.build_federated_averaging_process is by setting up each clients tf.data.Dataset with different parameters. For example limiting the number of steps with tf.data.Dataset.take. The guide tf.data: Build TensorFlow input pipelines has many more details.
Alternatively stopping based on a measurement of learning progress would require modifying some internals of the algorithm currently. Rather than using the APIs in tff.learning, it maybe simpler to poke around federated/tensorflow_federated/python/examples/simple_fedavg/ particularly the client training loop is here and could be modified to stop based on some criteria other than "end of dataset" (as currently used).
I'm actualy new in Machine Learning, but this theme is vary interesting for me, so Im using TensorFlow to classify some images from MNIST datasets...I run this code on Compute Engine(VM) at Google Cloud, because my computer is to weak for this. And the code actualy run well, but the problam is that when I each time enter to my VM and run the same code I need to wait while my model is training on CNN, and after I can make some tests or experiment with my data to plot or import some external images to impruve my accuracy etc.
Is There is some way to save my result of trainin model just once, some where, that when I will decide for example to enter to the same VM tomorrow...and dont wait anymore while my model is training. Is that possible to do this ?
Or there is maybe some another way to do something similar ?
You can save a trained model in TensorFlow and then use it later by loading it; that way you only have to train your model once, and use it as many times as you want. To do that, you can follow the TensorFlow documentation regarding that topic, where you can find information on how to save and load the model. In short, you will have to use the SavedModelBuilder class to define the type and location of your saved model, and then add the MetaGraphs and variables you want to save. Loading the saved model for posterior usage is even easier, as you will only have to run a command pointing to the location of the file in which the model was exported.
On the other hand, I would strongly recommend you to change your working environment in such a way that it can be more profitable for you. In Google Cloud you have the Cloud ML Engine service, which might be good for the type of work you are developing. It allows you to train your models and perform predictions without the need of an instance running all the required software. I happen to have worked a little bit with TensorFlow recently, and at first I was also working with a virtualized instance, but after following some tutorials I was able to save some money by migrating my work to ML Engine, as you are only charged for the usage. If you are using your VM only with that purpose, take a look at it.
You can of course consult all the available documentation, but as a first quickstart, if you are interested in ML Engine, I recommend you to have a look at how to train your models and how to get your predictions.
I am training a ssd_inception neural network using the Tensorflow Object Detection API. In the pipeline config file, there are preprocessor options to augment images during training. Is there any way to introduce probability of applying a given preprocessing? E.g 20% that the image will change contrast etc. If not, are there any plans to do so?
We don't have any current plan to do that. But feel free to send in a pull request. We are happy to review.
See https://github.com/tensorflow/models/blob/master/object_detection/builders/preprocessor_builder.py and https://github.com/tensorflow/models/blob/master/object_detection/core/preprocessor.py to get started.
I have just started experimenting with Deep Learning and Computer Vision technologies. I came across this awesome tutorial. I have setup the TensorFlow environment using docker and trained my own sets of objects and it provided greater accuracy when I tested it out.
Now I want to make the same more real-time. For example, instead of giving an image of an object as the input, I want to utilize a webcam and make it recognize the object with the help of TensorFlow. Can you guys guide me with the right place to start with this work?
You may want to look at TensorFlow Serving so that you can decouple compute from sensors (and distribute the computation), or our C++ api. Beyond that, tensorflow was written emphasizing throughput rather than latency, so batch samples as much as you can. You don't need to run tensorflow at every frame, so input from a webcam should definitely be in the realm of possibilities. Making the network smaller, and buying better hardware are popular options.