I am training my model to detect the vertebra but after training, it shows no result. Is there any way I can get the results by tuning any of the parameters?
Related
For example, I want to use YoloV5 to detect intestinal cells first and get green boudingbox, and then detect intestinal bulges in green boundingbox.
I want to import boundingboxes from the first yoloV5 model into the second YoloV5 model, but how do I get boundingboxes into the second YoloV5 model？
Or you have other good ideas.
I would train a new Yolov5 model on both classes. By doing so you would only need a single inference to get the results.
If that is not possible I would input the clean image into the different models. Gather the bboxes of the different models and finally plot them on the original image. This is so that the previous bboxes do not impact the inference process negatively as the models has most certainly never seen bounding boxes during training.
Since I am training and testing within same loop (for each epoch on training set, network is applied on entire validation set).
Now does it make sense that the highest validation accuracy I get at some instant (nth epoch) be my network's highest accuracy or should I only use the validation accuracy once the graph has settled and weights don't change?
I think you are confusing testing with validation. If possible you should keep a separate test set of your dataset for testing only AFTER the training and validation are done.
Although you can use that test set for your validation, it's the best practice to do inference on a separate set of data that the model has never seen before.
So answering your question, if you get a spike in your validation accuracy then it can or cannot be the highest as more the number of epochs more the chances of greater accuracy (assuming the model is not overfitting the dataset).
In this situation, it's best that you save the model after every validation accuracy spike so you the best model with the highest validation accuracy after the training and validation has completed.
I have trained three different models separately in caffe, and I can get the probability of belonging to each class for semantic segmentation. I want to get an output based on the 3 probabilities that I am getting (for example, the argmax of three probabilities). This can be done by inferring through net model and deploy.prototxt files. And then based on the final soft output, the hard output shows the final segmentation.
My questions are:
How to get ensemble output of these networks?
How to do end-to-end training of ensemble of three networks? Is there any resources to get help?
How to get final segmentation based on the final probability (e.g., argmax of three probabilities), which is soft output?
My question may sound very basic question, and my apologies for that. I am still trying to learn step by step. I really appreciate your help.
There are two ways (at least that I know of) that you could do to solve (1):
One is to use pycaffe interface, instantiate the three networks, forward an input image through each of them, fetch the output and perform any operation you desire to combine all three probabilites. This is specially useful if you intend to combine them using a more complex logic.
The alternative (way less elegant) is to use caffe test and process all your inputs separately through each network saving the probabilities into files. Then combine the probabilities from the files later.
Regarding your second question, I have never trained more than two weight-sharing CNNs (siamese networks). From what I understood, your networks don't share weights, only the architecture. If you want to train all three end-to-end please take a look at this tutorial made for siamese networks. The authors define in their prototxt both paths/branches, connect each branch's layers to the input Data layer and, at the end, with a loss layer.
In your case you would define the three branches (one for each of your networks), connect with input data layers (check if each branch processes the same input or different inputs, for example, the same image pre-processed differently) and unite them with a loss, similarly to the tutorial.
Now, for the last question, it seems Caffe has a ArgMax layer that may be what you are looking for. If you are familiar with python, you could also use a python layer that allows you to define with great flexibility how to combine the output probabilities.
I have a pretrained CNN (Resnet-18) trained on Imagenet, now i want to extend it on my own dataset of video frames , now the point is all tutorials i found on Finetuning required dataset to be organised in classes like
class1/train/
class1/test/
class2/train/
class2/test/
but i have only frames on many videos , how will i train my CNN on it.
So can anyone point me in right direction , any tutorial or paper etc ?
PS: My final task is to get deep features of all frames that i provide at the time of testing
for training network, you should have some 'label'(sometimes called y) of your input data. from there, network calculate loss between logit(answer of network) and the given label.
And the network will self-revise using that loss value by backpropagating. that process is what we call 'training'.
Because you only have input data, not label, so you can get the logit only. that means a loss cannot be calculated.
Fine tuning is almost same word with 'additional training', so that you cannot fine tuning your pre-trained network without labeled data.
About train set & test set, that is not the problem right now.
If you have enough labeled input data, you can divide it with some ratio.
(e.g. 80% of data for training, 20% of data for testing)
the reason why divide data into these two sets, we want to check the performance of our trained network more general, unseen situation.
However, if you just input your data into pre-trained network(encoder part), it will give a deep feature. It doesn't exactly fit to your task, still it is deep feature.
Added)
Unsupervised pre-training for convolutional neural network in theano
here is the method you need, deep feature encoder in unsupervised situation. I hope it will help.
I am using caffe to train a deep network, and have set the display at 200 iterations in my solver prototext file. However, instead of getting the loss and accuracy, I am getting a large number of lines of the form
solver.cpp:245] Train net output #{no.}: prob = {no.}.
The prob values are the probabilities of output of softmax layer(the last layer in my network).
This display is of no real use to me. I am interested in seeing only the rate at which the accuracy is evolving with the number of iterations. Can someone suggest a way in which I may print only the relevant parameters to stdout? Is there a way to control in general what is printed by caffe to stdout?
Thanks.
(Note: I am using caffe executable for training on ubuntu)