So I have followed this tutorial and retrained using my own images.
https://www.tensorflow.org/tutorials/image_retraining
So I now have an "output_graph.pb" and a "output_labels.txt" (which I can use with other code to classify images).
But how do I actually generate a confusion matrix using a folder of testing images (or at least with the images it was trained on)?
There is https://www.tensorflow.org/api_docs/python/tf/confusion_matrix
but that doesnt seem very helpful.
This thread seems to just be using numbers to represent labels rather than actual files, but not really sure: how to create confusion matrix for classification in tensorflow
And Im not really sure how to use the code in this thread either:
How do i create Confusion matrix of predicted and ground truth labels with Tensorflow?
I would try to create you confusion matrix manually, using something like this steps:
Modify the label_image example to print out just the top label.
Write a script to call the modified label_image repeatedly for all images in a folder.
Have the script print out the ground truth label, and then call label_image to print the predicted one.
You should now have a text list of all your labels in the console, something like this:
apple,apple
apple,pear
pear,pear
pear,orange
...
Now, create a spreadsheet with both row and column names for all the labels:
| apple | pear | orange
-------+----------------------
apple |
pear |
orange |
The value for each cell will be the number of pairs that show up in your console list for row, column. For a small set of images you can compute this manually, or you can write a script to calculate this if there's too many.
Related
So I have been looking at XGBoost as a place to start with this, however I am not sure the best way to accomplish what I want.
My data is set up something like this
Where every value, whether it be input or output is numerical. The issue I'm facing is that I only have 3 input data points per several output data points.
I have seen that XGBoost has a multi-output regression method, however I am only really seeing it used to predict around 2 outputs per 1 input, whereas my data may have upwards of 50 output points needing to be predicted with only a handful of scalar input features.
I'd appreciate any ideas you may have.
For reference, I've been looking at mainly these two demos (they are the same idea just one is scikit and the other xgboost)
https://machinelearningmastery.com/multi-output-regression-models-with-python/
https://xgboost.readthedocs.io/en/stable/python/examples/multioutput_regression.html
I am trying to use DeepLabV3 for image segmentation and object detection/classification on Coral.
I was able to sucessfully run the semantic_segmentation.py example using DeepLabV3 on the coral, but that only shows an image with an object segmented.
I see that it assigns labels to colors - how do i associate the labels.txt file that I made based off of the label info of the model to these colors? (how do i know which color corresponds to which label).
When I try to run the
engine = DetectionEngine(args.model)
using the deeplab model, I get the error
ValueError: Dectection model should have 4 output tensors!This model
has 1.
I guess this way is the wrong approach?
Thanks!
I believe you have reached out to us regarding the same query. I just wanted to paste the answer here for others to reference:
"The detection model usually have 4 output tensors to specifies the locations, classes, scores, and number and detections. You can read more about it here. In contrary, the segmentation model only have a single output tensor, so if you treat it the same way, you'll most likely segfault trying to access the wrong memory region. If you want to do all three tasks on the same image, my suggestion is to create 3 different engines and feed the image into each. The only problem with this is that each time you switch the model, there will likely be data transfer bottleneck for the model to get loaded onto the TPU. We have here an example on how you can run 2 models on a single TPU, you should be able to modify it to take 3 models."
On the last note, I just saw that you added:
how do i associate the labels.txt file that I made based off of the label info of the model to these colors
I just don't think this is something you can do for segmentation model but maybe I'm just confused on your query?
Take object detection model for example, there are 4 output tensors, the second tensor gives you an array of id associates with a certain class that you can map to a a label file. Segmentaion models only give the pixel surrounding an objects.
[EDIT]
Apology, looks like I'm the one confused on segmentation models.
Quote form my college :)
"You are interested to know the name of the label, you can find the corresponding integer to that label from result array in Semantic_segmentation.py. Where result is classification data of each pixel.
For example;
if you print result array in the with bird.jpg as input you would find few pixel's value as 3 which is corresponding 4th label in pascal_voc_segmentation_labels.txt (as indexing starts at 0 )."
I am noob in ML. I have a Person table that have,
-----------------------------------
User
-----------------------------------
UserId | UserName | UserPicturePath
1 | MyName | MyName.jpeg
Now I have tens of millions of persons in my database. I wanna train my model to predict the UserId by giving images(png/jpeg/tiff) in bytes. So, input will be images and the output I am looking is UserId. Right now I am looking for a solution in ML.NET but I am open to switch to TensorFlow.
Well, this is nothing but a mapping problem, particularly an id-to-face mapping problem, and neural nets excell at this more than on anything else.
As you have understood by now, you can do this using tensorflow, pytorch or any library of the same purpose.
But if you want to use tensorflow, read on for a ready code at the end. It is easiest to achieve your task by transfer learning, i.e. by loading some pretrained model, freezing all but last layer and then training the network to produce a latent one-dimensional vector for a given face image. Then you can save this vector into a database and map it into an id.
Then, whenever there is a new image and you want to predict an id for the image, you run your image through the network, get your vector and compute cosine similarity with vectors in your database. If the similarity is above some threshold and it is the highest among other similarities, you have found your id.
There are many ways to go about this. Sure you have to preprocess your data, and augment it at the same time, but if you want some ready code to play with then have a look at this famous happy house tutorial from Andrew NG and his team:
https://github.com/gemaatienza/Deep-Learning-Coursera/blob/master/4.%20Convolutional%20Neural%20Networks/Keras%20-%20Tutorial%20-%20Happy%20House%20v2.ipynb
This should suffice your needs.
Hope it helps!
When training a single class object detector in Tensorflow, I am trying to pass instances of images where no signal object exists, such that the model doesn't learn that every image contains at least one instance of that class. E.g. if my signal were cats, id want to pass pictures of other animals/landscapes as background -this could also reduce false positives.
I can see that a class id is reserved in the object detection API (0) for background, but I am unsure how to code this into the TFrecords for my background images - class could be 0 but what would be the bounding box coords? Or do i need a simpler classifier on top of this model to detect if there is a signal in the image or not, prior to detecting position?
Later approach of simple classifier, makes sense. I don't think there is a way to do the first part. You can use check on confidence score as well apart from checking the object is present.
It is good practice to create a dataset with not objects of interest, for the same you need to use the same tools (like - label img) that you have used for adding the boxes, image with no BB wil have xml files with no details of BB but only details of the image. The script create tf record will create the tf record from the xml files, look at the below links for more inforamtion -
Create tf record example -
https://github.com/tensorflow/models/blob/master/research/object_detection/dataset_tools/create_pet_tf_record.py
Using your own dataset-
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md
I download the following graph-cut code:
https://github.com/shaibagon/GCMex
I compiled the mex files, and ran it for pre-defined image in the code (which is rgb image)
I wanna optimize the image segmentation results,
I have probability map of the image, which its dimension is (width,height, 5). Five probability distribution over the image dimension are stacked together. each relates to one the classes.
My problem is which parts of code should according to the probability image.
I want to define Data and Smoothing terms based on my application.
My question is:
1) Has someone refined the code according to the defining different energy function (I wanna change Unary and pair-wise formulation).
2) I have a stack of 3D images. I wanna define 6-neighborhood system, 4 neighbors in current slice and the other two from two adjacent slices. In which function and part of code can I do the refinements?
Thanks