Image Classification using CNN Keras | Full implementation
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- Опубликовано: 3 июн 2024
- In this video, we will implement Image Classification using CNN Keras. We will build a Cat or Dog Classification model using CNN Keras.
Keras is a free and open-source high-level API used for neural networks. Building a Deep Learning model in Keras is fast and easy.
I already covered the full detailed mathematical theory behind the Convolutional Neural Network (CNN). If you haven't checked that playlist, then you can find its link down here.
For now, let's first see Image Classification using CNN Keras.
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Timestamps:
0:00 Intro
1:43 Imports
3:25 Loading Dataset
6:30 Model Implementation using keras
15:36 Predictions for individual images
17:17 End
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Follow my entire playlist on Convolutional Neural Network (CNN) :
📕 CNN Playlist: • What is CNN in deep le...
📕 Programming Assignment: github.com/Coding-Lane/Image-...
📕 Dataset: bit.ly/ImgClsKeras
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✔ Complete Neural Network Playlist: • How Neural Networks wo...
✔ Complete Logistic Regression Playlist: • Logistic Regression Ma...
✔ Complete Linear Regression Playlist: • What is Linear Regress...
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If you want to ride on the Lane of Machine Learning, then Subscribe ▶ to my channel here: / @codinglane
The quality of content, simplicity in the explanation, teaching from the basics, explanation of the dimensions and model architecture parameters; everything about the playlist is so amazing. Great job man!!
Playlist suggestion: 1D CNN on time series data passing big window-sized data (time dimension) along with a multi-headed neural network targeting classification and regression simultaneously is something I would love to see.
Maybe best explanation on YT on this topic, i am looking at hours of content and this 18 min video helped me a ton, Thank you!
No one can give that much amount of Explaination thank you🙏🙏🙏
Everything is simple and straightforward. keep up the good work bro!
Thank you so much, brother, for this STELLAR series on CNNs. Without a doubt, the BEST on RUclips. Your efforts do not go unnoticed. Please keep making high quality content. Cheers from Austin, Texas!
Hey… thanks a lot for this. I really appreciate it!! 🤗
Thanks for your effort and time in creating such great content. I have completed the whole playlist and learned the fundamentals of NN. Thanks again! Keep creating, teaching and sharing:)
That's amazing the way you have thought all the playlist was outstanding, really helped me and cleared lots of my confusions
Respect from Afghanistan
After searching a lot I came across this video. This was very clear and easy. Thanks a lot
Happy to help 🤗
Explaining everything from the basics is extremely useful...especially in deep learning.
Happy to help!
How can I get code
i watched your entire playlist its pretty amazing the way you have explained everything it went in my mind without any resistance.... thanks a lot its a great help....... You are really good at teaching keep it up 🔥🔥😍😍
This is one of the best explanations i just finished the whole playlist thank you so much for your efforts
Glad it was valuable 😇
This is MUCH easier to understand than the elite university certificate program I am currently in for Deep Learning.
Simplicity at its peak ❤️🔥
Thank you. You are a very nice person and easy to learn these easy concepts from.
Hats off to the excellent explanation. Great job !!!
Your videos are awesome. So helpful. One stop for knowledge seeker. Can you please make videos on SVM, GMMs, Maximum Likelihood estimation as well?
Thank you so much for your playlist, it has been so usefull for me ! I hope that you're doing well :)
You r Great .. This model very Effective Thank you
man your videos are absolutely crazy good. I love your teaching style. I hope you will keep going. :)
Thank you so much! Appreciate your comment
@@CodingLane hey buddy, great video. please make video on TFOD installation in local system for object detection as I haven't found any specific video on RUclips
To the point explanation. Well done brother❤
Really informative. Thankyou
Really great content bro , in simplest English as if I am listening in Hindi. Very good
Thank you so much for your fantastic video! You are truly amazing.
Thank you so much for your efforts. It is the best playlist explaining CNN
Thank you so much!
gotit... I usually don't comment but this video definitely deserve a round of applause... You have explained it the best possible way. Many thanks! 🙂
Thank you so much… it means a lot to me.
thank you for all these videos,clear and very helpful!
can you make also videos about few-shot learning?
This was so Helpful
Thanks for that
Sending you my Love From IRAN
Thank you very much for this video. I request you to do videos on all machine learning algorithms.
Thank you
Thankyou bhaiya!! I got output
Absolutely great, double thumbs up!
Found it very helpful, thanks a lotttt for creating this video sir
You’re welcome 😇
Very nice thanks a lot! Please upload more videos, very helpful!
You’re welcome 😇
Excellent interpretation, but I can not download the dataset. It says "This site can’t be reached". What can I do?
while fitting the model, how do we get to know that when we have to stop re-running epochs count for better accuracy? like by doing it again and again we can reach to desired accuracy level...
This is really helpful.. thanks so much!
But I'm unable to download the dataset completely,it's saying no access; is there another way I can get the dataset downloaded?
Bro love you.....virtual hug from me...thank you sooo much bhai.....
Thanks and God bless you. I really appreciate your video.Please can you do a video on any pretrained network with svm for classification
i enjoyed your all videos on CNN
Glad that you enjoyed!
did you use MobileNet architecture for the CNN model?
you are amazing man
Please do videos on RNN also. Your videos really useful. Thank you.
Saras bhanave 6 bhai tu.....gamyu ane avdyu badhu video joine.......
Thank you bhai… amen pan Gujarati j che!
thank you so much sir
Great work brother, is there any video where u have implemeted using tensor flow frame work?
amazing series bhai!
Thanks!
good content in short time
Thank you very much
Brother, this video has been an enormous help to me. I'm doing my thesis to get the Mechatronic engineering degree on DL, which is how to be a specialist in AI in postgraduate.
Greetings from Mexico.
Greetings! Glad it was helpful to you 😇
At 6:33 when i am running it i am getting black images no the image of dog or a cat how to resolve it can anyone tell
excellent content
how did you upload those images?
and how did you make a csv file?
please dont use shortcuts I need to know this in details help me with it asap!!!
Hey Bro,
Loved your entire playlist! It was really helpful.
I had a question though. In the end, if the probability for one of the dog images was below 0.5, does that mean that all dog images will have a probability of being less than 0.5? If no, then how are we using a fixed threshold for classification? Can it not lead to erroneous classification too?
Hi, I found your video very educative. Can you please demonstrate how CNN can be applied on cellular network for DDoS detection
In input.csv file datasets it shows "Wrong number of columns at line 6" error
Very informative
great explanation bro 😇😇😇😇😇😇🤩
amazing
Hello, your video did help me a lot. Thank you so much. But its was possible only because of the dataset which you have provided. Kindly guide on how to have such datasets for different classification?
So what we do when we want multi class output ,, which activation function we use can u explain
Very nice thanks a lot
but I have a little problem
invalid shape (1,) for image data
and I don't know how to solve it
if you can help me, please
how can I use it as pedestrian detection and how to find the pedestrian data set
nice content
thanks bro
Thanks
How do I convert a folder of images (probably each has different resolution ) into a trainable .csv file?
When I run the loading dataset part i,e X_train = np.loadtxt it says wrong number of columns at line 2
Actual lifesaver
Awesome explanation. Good work
Thank you!
Sir can you make a video on multiclass label image classification using vision transformer
Jay, I can't download the dataset. Please help ...
hello....in case you have no csv ....and your dataset contains small images (40 ) ...how to download data set with Tensor flow ???
Trying to install tensorflow becaise of user pernissions denied and path not mentioned..please help me
Please make a video for low-light Image enhancement using CNN
Bro could you explain vision transformer with example and creating one transformer base image classification model from scratch
Nice explanation
Thank you!
Nice job
Thanks!
thanks bro , nice one
Always welcome
Hello I'm not seeing the link to a dataset
You are too much. Best among equal, thanks for this video. please is it possible for you to replace the fully connected layer with svm or any other machine learning algorithm. i need a video of the implementation on that, Thanks i really appreciate
the dataset is in numerical value how it convert to numerical and how we see
bro when are you going to upload more videos? very helpfull
thanks dude
can you show the severity level of the disease using CNN multiclass model?
i want dataset dataset link is not working
Bro how did u convert all images to csv files
Thanks 👍🏿
Your Welcome!
You are treasure
At 6:33 when i am running it i am getting black images no the image of dog or a cat how to resolve it can anyone tell
please tell from where u collected the dataset??... I want to collect the dataset of tree images
I don't have the link from where I collected the dataset. But you can find datasets on Kaggle. Or you can also search online for datasets. They are easily available as long as you don't require very large database.
Very interesting, How about CNN IMAGE PROCESSING VIDEOS
Hello Thank you for this wonderful tutorial. I just wanted to ask at 5:43 you divided all those values with 255 as I am beginner I had question like why did you divide with 255 ? It would be great if you could explain a bit. Thank you for the tutorial by the way.
To normalize the data values
hey there the short link isnt working anymore cannot get a hold on the dataset
Yes… I it got deleted accidentally… I will upload it again
please what is the code for the learning curves I need today please someone help
hi.. awesome video. can you put video on steganalysis coding
Thanks for the suggestion… I will see if I can make video on it
@coding lane, and everyone.. Can someone please assist me, I have a problem when it comes to fitting my model during training... How can I fix the "Invalid-Argument-Error? I have followed all the steps from this video, but I still get the same error. Any suggestion, on how to solve the error?
Is the images labelled?
great tutorial brother just make one video on how to use this in django and save model
Thanks for the suggestion… will try something like this may be in the future
when i try to load the dtaset it says name 'np' is not defined. But i hv downloaded the dataset ald but quiet confuse on where to put the datasets
You need to install numpy using pip or conda, whatever environment you are using. Try running the following command in your command prompt "pip install numpy" if you are using pip. Also search online about how to install numpy on your system
I've a question, have you used deep learning??
Can we do this classification in real time using webcam
sir, i dont understand how to load the dataset? which folder should i put the dataset in so it can actually load them? pls reply
You need to put your dataset in the directory where your code is present. For eg, if your code is in folder "C:/users/username/desktop/mycode", then your dataset should also be unzipped in the same "mycode" directory i.e., at "C:/users/username/desktop/mycode". After unzipping, your paste, the dataset files in "mycode" folder.
So mycode folder must look like (considering your code is written in ImageClassification.ipynb):
mycode>
ImageClassification.ipynb
input.csv
input_test.csv
labels.csv
labels_test.csv