Glad it was helpful!

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Yess!!

My pleasure!

Glad to hear that!

Thanks a lot!

No detail is too small for deep learning; chances are if you can see them the algorithm can also see them.

Welcome

Just switch the line where we have Random Forest to SVM.

I will try to do a video on the topic of PCA.

@@DigitalSreeni looking forward to the video

hi, i have been reading a paper and it say this "We noticed that randomly selecting few dimensions is more efficient that a classic Principal Component Analysis (PCA) algorithm [30]. This simple random dimensionality reduction significantly decreases the complexity of our model for both training and testing time while maintaining the state-of-the-art performance." Maybe can help u

May be you are are missing something. The approach you mentioned is deep learning approach which works great if you have large amount of training data. With limited training data, you will get higher accuracies with feature engineering and traditional machine learning (e.g., Random Forest). You can engineer your features by adding a bunch of filters or filter banks using Gabor. Or you can use pretrained CNN (e.g., VGG on imagenet) as feature extractor, which is what I described in this video. In summary, this approach may yield better results with only a handful of training images and masks.

You are welcome!

@@DigitalSreeni I have a doubt on this here we are giving input size 256, but the default size of the vgg16 is (224,224) based on this only the convolutional layers are structured and got 92 % accuracy on imagenet competition. Here we are changing input shape will it affect the performance of the model?

I actually bumped into your lesson 268 and I am reviewing it. Thanks

Sure. You can use output from trained models as features for your traditional classification algorithms such as SVM or RF or Gradient boost.

@@DigitalSreeni thanks a lot, Sir. But I have another question. In transfer learning we see that we can do the augmentation manually by using ImageDataGenerator of keras during running the model. But is their any way to do augmentation manually in hybrid model like this u given in this video??? If it can be done, then how?? Can u please tell us??

@@DigitalSreeni did you get any luck trying the SVM?

to me its showing 0.0 accuracy in two class problem

Well, that is why you have neural networks, try SGD classifier. If you want to stick with SVM try using PCA to reduce dimensions.

@@DigitalSreeni Thank you for your reply!

I have the same problem,plz if you had resolved it tell me

I need same issue

May be an issue with keras.... instead of from keras.models import ..... from keras.layers import ...... try... from tensorflow.python.keras.layers import .... from tensorflow.python.keras.models import .... But, please be consistent. Do not mix some modules from keras and some from tensorflow.keras.

Thank you very much.

It depends on whether you define the problem as multiclass with 2 classes or binary. If you define it as multiclass and using softmax activation then you need to encode your labels and convert to categorical. If you are defining it as binary and using sigmoid activation, you do not need to convert to categorical, just encoding to 0 and 1 would do.

@@DigitalSreeni thankyou so much, your video helps me a lot

Yes, no reason why it wouldn't work for 30 classes.

same here...how did u resolve the issue???

@@ritujangra00 Did anyone solve it?

I second this, transfer learning from Unet to random forest for image segmentation would be amazing

When we say we want to use pretrained models as feature extractors we will set trainable=False because we want to use pre-trained weights as kernels that define the feature extractor. If you do the training, running through epochs, you are adjusting the weights and fitting a model. This is not the goal when you only want to use the model for feature extraction. Think of it as training a model on a bunch of data and now you are using the trained model for prediction. Except, we are not predicting and only extracting features because the original network was trained for a different purpose.

@@DigitalSreeni thank you for this valuable help and contributions

To save and load sklearn models you can use Pickle. import pickle #Save the trained model as pickle string to disk for future use filename = "sandstone_model" pickle.dump(model, open(filename, 'wb')) #To test the model on future datasets loaded_model = pickle.load(open(filename, 'rb')) result = loaded_model.predict(X) If you accuracy is not improving, play with the classifier parameters. If that doesn't help, you need more training data. You cannot augment your way to better accuracy.

Just replace the Random Forest part in this video with SVM or other classifier. I am not sure if it requires a new video for each classifier in this process.

@@DigitalSreeni Thanks. I was able to do it. Please post a video with pretrained AlexNet.

@@ameentendolkar4430 what did u replace in the code to use SVM as I'm trying to do the same

I don't see why there should be an issue with 2 class classification. It just reads the folder name and assigns it as a label. It does not matter how many folders you have. Please make sure the folder names do not have any spaces, that can lead to issues.

@@DigitalSreeni no sir no spaces in folder names... Just changed the folder path as I am running this on colab...just troubled with the accuracy..is it because I am using xray images?. Plz have a look..

I have the 3problem ,plz if you had resolved it tell me

@@anupriya3741 i also got 0.0 accuracy with 10 class dataset...what can be done??

@@DigitalSreeni i also got 0.0 accuracy with 10 class dataset...what can be done??pls rply sir....m stuck

For a couple of reasons, primary reason being Random Forest is a tree-based model so it does not require feature scaling.

Thank you very much.