For the toy example A2 part of the Beta 12 Release, it is said that there are two option for training:
A2_RunCntk_py3.py (python API)
A2_RunCntk.py (brain_script)
Are the models trained from these two methods the same? Or in other words, can I load the model from brain_script into python API and then detect other testing images?
Also see Object Detection using Fast R CNN.
Yes it is possible to use Python to load a model you trained with Brainscript. A few gotchas in doing this correctly are described here. We are working on making things work seamlessly without too much Python code for massaging the data.
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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 a Teachable Machine model which i trained to recognize some specific objects, the issue with it, however, is that it does not recognize when there is nothing, basically it always assumes that one of the objects is there. One potential solution I am considering is combining two models like the YOLO V2 Tflite model in the same app. Would this be even possible/efficient? If it is what would be the best way to do it?
If anyone knows a solution to get teachable machine to recognize when the object is not present that would probably be a much better solution.
Your problem can be solved making a model ensemble: Train a classifier that learns to know if your specific objects are not in the visual space, and then use your detection model.
However, I really recommend you to upload your model to an online service and consume it via an API. As I know tflite package just supports well MobileNet based models.
I had the same problem, just create another class called whatever you want(for example none) and put some non-related images in it, then train the model.
Now whenever there is nothing in the field, it should output none.
this is a basic question.
Currently I am using one available 1.x model for object detection.
For this I am re-training this model with my own data and can detect the objects I want.
I would like to port all my logic to 2.x version in order to use the new released tools for converting models to TFLite.
Do I need to retrain the weights of the reference model (coco for example) once I modify the code to 2.0 ?
Or only retrain my customized data ?
if yes, is there any recommendation to do this without much effort ?
Thanks for the advice
Luckily for all users, tensorflow has a lot of documentation and the developers of tensorflow thought you would ask this question and therefore have answered it already for you. This post should help you perfectly migrating your model from 1.x to 2.x.
To sum it up quickly, if you are using high level APIs like keras it is basically no work at all. If you want to make use of of the performance improvements made in tensorflow 2 or if you are not using said high level APIs it might be a bit more work.
Weights of your network should generally not have to be retrained, except if you want to change your model obviously. If you want to just use the same model but then in tensorflow 2, the link above should help you transfer your code to tensorflow 2 and you should not have to retrain the weights of your model.
I want to use pre trained models in my go application. Especially the Inception-ResNet-v2 model.
This model seems to be only available via tensorflow hub (https://www.tensorflow.org/hub/).
However I could not find any documentation how to use tensorflow hub with the go language bindings for tensorflow.
How can I download and use these models in go?
So after a lot of work in the past few days I finally found a way.
At first I wanted to just use Python to do all the Tensorflow stuff and then provide the results via a rest service. However it turned out that the number of models provided by Tensorflow Hub is very small. This was a problem for me because I had to try out different models and compare them.
Thus I switched to using models from https://github.com/tensorflow/models. There are several tutorials how to export the data to .pb files. Those files can then be loaded in Go using gocv.
It requires a lot of work to convert the files, but in the end I think this is the best way to use Tensorflow models in go.
I am very new to CNTK.
I wanted to train a set of images (to detect objects like alcohol glasses/bottles) using CNTK - ResNet/Fast-R CNN.
I am trying to follow below documentation from GitHub; However, it does not appear to be a straight forward procedure. https://github.com/Microsoft/CNTK/wiki/Object-Detection-using-Fast-R-CNN
I cannot find proper documentation to generate ROI's for the images with different sizes and shapes. And how to create object labels based on the trained models? Can someone point out to a proper documentation or training link using which I can work on the cntk model? Please see the attached image in which I was able to load a sample image with default ROI's in the script. How do I properly set the size and label the object in the image ? Thanks in advance!
sample image loaded for training
Not sure what you mean by proper documentation. This is an implementation of the paper (https://arxiv.org/pdf/1504.08083.pdf). Looks like you are trying to generate ROI's. Can you look through the helper functions as documented at the site to parse what you might need:
To run the toy example, make sure that in PARAMETERS.py the datasetName is set to "grocery".
Run A1_GenerateInputROIs.py to generate the input ROIs for training and testing.
Run A2_RunCntk_py3.py to train a Fast R-CNN model using the CNTK Python API and compute test results.
The algo will work on several candidate regions and then generate outputs: one for the classes of objects and another one that generates the bounding boxes for the objects belonging to those classes. Please refer to the code for getting the details of the implementation.
Can someone point out to a proper documentation or training link using which I can work on the cntk model?
You can take a look at my repository on GitHub.
It will guide you through all the steps required to train your own model for object detection and classification with CNTK.
But in short the proper steps should look something like this:
Setup environment
Prepare data
Tag images (ground truth)
Download pretrained model and create mappings for your custom dataset
Run training
Evaluate the model on test set