158b - Transfer learning using CNN (VGG16) as feature extractor and Random Forest classifier

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This video explains the process of using pretrained weights (VGG16) as feature extractors for traditional machine learning classifiers (Random Forest).

Code generated in the video can be downloaded from here:

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I got so many ideas watching you walk through transfer learning step-by-step. 🙏🙏

wayne

Thorough explanation! Really helpful and informative. Thanks 🙏

shilpamanocha

You're damn great sir best tutorial on YouTube

samarafroz

Awesome thankyou so much keep making more videos

😊😊😊😊😊😊😊

diptilandge

Thank you so much for the amazing tutorial!! It was very informative and helpful!!

AIdAssist

looking forward to viewing and trying this on my own data. I haven't enough data to classify the images and the images are unusual.

inhibited

you have the pictures of barns and landscapes. If you were trying to distinguish between a person with freckles and the same person without freckles, is the detail too small for ML to pick it up?

inhibited

Is there video of you explains the process of using pretrained weights (VGG16) as feature extractors for MLP?

hulk

Dear thank you very much for this video. I have implemented the same codes on brain tumor dataset but getting the accuracy result of 0, what should I do ? Also I have 4 class labels but confison matrix shows 7 labels. Could you please help me ?

ecemiren

For petrografic semantic segmentation for multiclass classification, im having a problem while fitting the model.
As the VGG16 output is of shape (None, 8, 8, 512), i cant fit it to my y_train values, of shape (None, 256, 256, 6).
What should i do?

bielmonaco

Hello Sreeni,
Thanks for your great efforts.
Please let me know how one can use SVM instead of Random Forest.
Also, I think feature vectors are of higher dimensions, so how can we implement PCA to reduce dimensions.

Best regards.

abderrahmaneherbadji

Thsnk you for this video ...I want to ask if I can extract features from 960 image using HOG and then concatenate with cnn features to obtain features vector to use it for classifying images using svm .please give us example about this idea

suramonther

Hi, sir. 28 number line, print (label). What is the output of this line? Same as directory_path?

shakirinjahanmozumder

after feature extraction, can i use the optimization like gridsearchcv or GA with tpot for the classifier?

iphipi

Kindly tell us how to do the feature selection using the MRI IMAGE DATASET

senthilkumar-uj

I wish you'd make a version of this with data augmentation (giving the input to random forest)

zohalghasemzadeh

Sir, using a large dataset my program gets crashed, because of the RAM isn't sufficient enough. What should I do?
Note: I'm using Colab

efadsheikh

I am wondering how you deploy this on a website if you want to classify an image from your iphone? I can save an h5 file using video 158, but don't see how to save it on this vgg program.

inhibited

Awesome video sir! I What if we have a big dataset? SVM from sklearn dont support generators or partial_fit

konstantinosdiamantis

Why do you perform on-hot encoding if you will not use it in the rest of the code?

pedroramon

158b - Transfer learning using CNN (VGG16) as feature extractor and Random Forest classifier

158b - Transfer learning using CNN (VGG16) as feature extractor and Random Forest classifier

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