chart_yolov4_custom, this is my trained loss/mAP graph
Why graph is showing only part I think is because I restart training in 2、3 times, so I think this is the last time I'vd trained.
My question is. Is there still a chance to plot whole 6000 steps loss/mAP graph?
Or I only can training without interruption?
Thank you guys and looking for your answer!!!
It happens because you trained the model several times. There is no solution except you train the model once again but without interruption.
Related
I use Faster RCNN to classify 33 items. But most of them are misclassified among each other. All items are snack packets and sweet packets like in the link below.
https://redmart.com/product/lays-salt-and-vinegar-potato-chips
https://www.google.com/search?q=ice+breakers&source=lnms&tbm=isch&sa=X&ved=0ahUKEwj5qqXMofHfAhUQY48KHbIgCO8Q_AUIDigB&biw=1855&bih=953#imgrc=TVDtryRBYCPlnM:
https://www.google.com/search?biw=1855&bih=953&tbm=isch&sa=1&ei=S5g-XPatEMTVvATZgLiwDw&q=disney+frozen+egg&oq=disney+frozen+egg&gs_l=img.3..0.6353.6886..7047...0.0..0.43.116.3......1....1..gws-wiz-img.OSreIYZziXU#imgrc=TQVYPtSi--E7eM:
So color and shape are similar.
What could be the best way to solve this misclassification problem?
Fine tuning is a way to use features, learned on some big dataset, in our problem, which means instead of training the complete network again, we freeze out weights of the lower layer of the network and add few layers at the end of network, as per requirement. Now we train it on our data-set again. So the advantage here is that, we don't need to train all-millions of parameters, but few only. Another is that we don't need large-dataset to fine-tune.
More you can find here. This is another-useful resource, where author has explained this in more detail(with code).
Note: This is also known as transfer-learning.
I have a few doubts about Tensorflow Object Detection API. Hopefully someone can help me out... Before that, I need to mention that I am following what sendex is doing. So basically, the steps are come from him.
First doubt: Why we need test.record for training? What it does during training?
Second doubt: Sendex is getting images from test.record to test the newly trained model, doesn't the model already knew that images because they are from test.record?
Third doubt: In what type of occasion we need to activate drop_out (in the .config file)?
1) It does nothing during training, you dont need that during training, but at certain time the model begins to overfit. It means the loss on training images continues to go down but the accuracy on testing images stops improving and begins to decline. This is the time when it is needed to stop traininga nd to recognise this moment you need the test.record.
2) Images were used only to evaluate model during training not to train the net.
3) You do not need to activate it, but using dropout you usually achieve higher accuracy. It prevents the net from overfitting.
I'm having some learning experience on tensorflows estimator api. Doing some classification task on a small dataset with tensorflow's tf.contrib.learn.DNNClassifier (I know there is tf.estimator.DNNClassifier but I have to work on tensorflow 1.2) I get the accuracy graph on my test dataset. I wonder why there are these negative peaks.
I thought they could occur because of overfitting and self repairing. The next datapoint after the peak seems to have the same value as the point before.
I tried to look into the code to find any proof that estimator's train function has such a mechanism but did not find any.
So, is there such a mechanism or are there other possible explanations?
I don't think that the Estimator's train functions has any such mechanism.
Some possible theories:
Does your training restart anytime? Its possible that if you have some Estimated Moving Average (EMA) in your model, upon restart the moving average has to be recomputed.
Is your input data randomized? If not, its possible that a patch of input data is all misclassified, and again the EMA is possibly smoothing out.
This is pretty mysterious to me. If you do find out what the real issue is please do share!
I'm new to tensorflow and the inception model. I found the following tutorial online (https://www.tensorflow.org/versions/master/how_tos/image_retraining/) and wanted to test this on my own project.
I'm trying to let the model recognize ethnicity based on people in a picture. I have made a training set of approx 850 images per category.
For some reason I'm unable to get more than a 65% accuracy level. I tried increasing the training steps and amount of images as well.
Maybe the inception model is not the correct model to use for this?
Could someone point me in a good direction of what I can do to improve the results?
Regards,
P.
Do you get 65% accuracy on the train or on the test set?
If it is on the train set, you are probably doing something wrong with your code.
If it is on the test set, you are indeed using the wrong model. The Inception model is a very very big model, and having only 850 images per category won't give you a good general model. It will simply "remember" those 850 images. (think of remembering the answer to each question on a test, instead of learning for a test)
Maybe you can try building a simpler, smaller model first and see how well that model learns!
I am facing a very strange problem where I am building an RNN model using tensorflow and then storing the model variables (all) using tf.Saver after I finish training.
During testing, I just build the inference part again and restore the variables to the graph. The restoration part does not give any error.
But when I start testing on the evaluation test, I always get same output from the inference all i.e. for all test inputs, I get the same output.
I printed the output during training and I do see that output is different for different training samples and cost is also decreasing.
But when I do testing, it always gives me same output no matter what is the input.
Can someone help me to understand why this could be happening? I want to post some minimal example but as I am not getting any error, I am not sure what should I post here. I will be happy to share more information if it can help the issue.
One difference I have between the inference graph during training and testing is the number of time steps in RNN. During training I train for n steps (n = 20 or more) for a batch before updating gradients while for testing I just use one step as I only want to predict for that input.
Thanks
I have been able to resolve this issue. This seemed to be happening as one of my input feature was very dominant in its original values due to which after some operations all values were converging to single number.
Scaling that feature has helped to resolve this.
Thanks
Can you create a small reproducible case and post this as a bug to https://github.com/tensorflow/tensorflow/issues ? That will help this question get attention from the right people.