Hello everyone.
So I watched the first video and I wanted play around with something. I had already worked with Facial Expression Detection before, so I wanted to test it with resnet.
The challenge is on Kaggle for the FER2013 dataset. I’m going to be posting updates here.
So I tried hard to train my model on the dataset directly. After multiple sessions of resnet34 and resnet50 (+/- unfreeze) the maximum accuracy I achieved was 50%.
When I plot top losses I find some images have problems like being blank etc. So I am trying to clean the data by running OpenCV DNN Face Detector on the dataset and removing those faces with confidence < 50%.
There are 28k images with 7 classes in here. Also the distribution of images in the classes is very bad. Do I need to take equal from each class to better train ? Or whole (cleaned) ?
I cleaned the dataset and about 3k images were removed. So I have 25k images left now.
The distribution is like this:
| angry | disgust | fear | happy | sad | surprise | neutral |
|---|---|---|---|---|---|---|
| 3577 | 401 | 3565 | 6645 | 3985 | 2946 | 4584 |
which has very less items of the class disgust. Any suggestion on what to do ? I trained the model now and it gives me a decrease of just 2% error rate, i.e earlier error was 50% ish and now its 48% ish.
What does the confusion matrix look like?
Does your data after cleaning look decent?
Would you mind sharing your code, so we can see your model in more detail?
Okay, here is the colab notebook link: https://colab.research.google.com/drive/1b3eLAQ_7w-tbjgNVJjWHhqlY0j4GiKdX
I also nave the confusion matrix plotted in there.
Wait, what. Why are the labels getting distributed. It should retain parts of all labels for train and for validation right.
I cannot seem to connect to your notebook anymore. I did connect some hours ago once.
What do you mean by the labels are getting distributed?
I don’t understand what you mean by not being able to connect. Can you elaborate.
So when the data is split for training & validation, its split 70-30 or 80-20 but different classes are distributed equally right. That was my question. By labels, I meant classes.
But after some thought, I think that the Train in ImageDataBunch is just showing the labels for the first 5 images, not the classes which were given as input data.
The url to your notebook was not loading, but I opened it on a different computer, and it works fine.
You can specify the train-test proportion in your databunch argument, and you are right, it is showing only the first several items.
I don’t think they are equally distributed based on labels though, they are randomly distributed.
So you probably don’t need random.shuffle before putting them into databunch.
Just looking at your notebook, I think you might want to play around with different learning rates, instead of just passing .fit_one_cycle(10).
Also, seems like your model is underfitting since training loss is higher than validation loss.
Increase the number of epochs as well?
In the ten cycles, the train_loss, val_loss and error_rate follow a pattern like - decrease very fast, slow down, stop, start increasing back. Thus, I thought that 10 epochs would be better or I could be facing overfitting.
Also, in the first lesson, it worked fine on the Oxford dog dataset with 10 epochs on resnet50. That also was in my consideration.
