if you liked the content please support by subscribing 😇 1. here is the video for multiclass:---- ruclips.net/video/1Gbcp66yYX4/видео.html 2. here is video for object detection with tensorflow:----- ruclips.net/video/_TCUPl3j2kI/видео.html 3. here is video for object detection with YoloV3:------ ruclips.net/video/zm9h4mYymk0/видео.html
Great tutorial!!! thanks. Here, I noticed you didn't normalize your test data, don't you think this might have had a negative impact on your prediction in some way? Since your model was trained and evaluated on normalized data. Although at 1st glance it doesn't seem so.
Hello sir, How to upload only one data set folder like chech happy or not no need to check the saad, just happy folder so what channges i have to make in code
i need to check weather this is a plant leaf or not for my semester project so it will alot of help if you tell the code for single data set that the given image is the same or not in testing
This is the exact tutorial I am looking for. Thank you very much. You described all the steps in the most simplified way. This tutorial will help me a lot in my project so thank you again.
This is most awesome and most humble tutorial I've ever seen. Despite many other tuts that more like "watch me code" and throwing a line of code with complex variable naming to show off. Thank you.
After being stuck a whole day, I prayed for wisdom and bumped into your video. You are an answered prayer. Very grateful for your content. Keep at it. #NewSub
🎯 Key points for quick navigation: 00:14 *Train a neural network to classify mood (happy/not happy) using custom images from Google Photos.* 01:34 *Organize images into training, testing, and validation folders for efficient model training.* 05:31 *Use `ImageDataGenerator` in TensorFlow to preprocess and label images automatically.* 09:00 *Design a convolutional neural network (CNN) for image classification, including convolutional and max pooling layers.* 11:19 *Compile and train the model using binary cross-entropy loss and RMSprop optimizer.* 13:55 *Achieve 100% accuracy on a small dataset; discuss implications and potential improvements.* Made with HARPA AI
Thank You bro. After building 3 models I forgot the most basic thing, prediction on single random image file. Your video solved my issue. Much love from my side.
Excellent I just finished it and it recognized most of my images (maybe could it have recognized everyone if I had used more images for training), thanks a lot.
Thanks a lot for the amazing video. I tried it out for healthy and diseased plants, it looks like it wrongly identified few. Should i put them back in training folder and re-run everything again? Please suggest.
Thank you very much sir, you explained step by step. but I have problem in last step. how to accept sub directory path and select both folder data set. please reply me. Thank you.
Hey, I like your video a lot. However, at 4:30, how exactly did you call this image from your folder? I can't quite seem to figure it out as you didn't show exactly how you called it. Also, when I type in "img = image.load_img("basedata/train/happy/3.PNG"), it's telling me that the file isn't found and there is no such file, even though there is since I created it. Lastly, when I type in "plt.imshow(img)", it's telling me that the name 'img' is not defined. Please help...I'm following your video and this is throwing me off. Thanks
Hello sir, it worked really well till model fitting. For 'model_fit' step, it is throwing an error called "AttributeError: 'DirectoryIterator' object has no attribute '_assert_compile_was_called' "
Sir, My final year project is related to Automated Fruit Ripeness Detection using Deep Learning model. There will e at least four fruits , we have to train model to classify fruit either Underripe, ripe or overripe. Can you please help me. Can I make a GUI we-based in Flask in Google Collab??? or I should make Web Flask app using VS code and then integrate my model at Google Collab? Which will be more convenient for me? One more thing in your video there are two cases(happy or not happy) and you kept it binary. Is there any ternary option available for three classes? underripe, ripe and overipe?
Thank you very much. You made my day .I am happy to learn. Sir please upload more videos. Can you please send me code for model evaluation for same program
Sir, I got question regarding where did you save your photos initially? Per my understanding, you have put them into training folder. However, down the video, "there's 11 images in our validation dataset", you're saying at 8:40... I am confused: should I copy images to validation folder, too?
Very helpfull tutorial. I have some questions though. Shouldnt all the images of the dataset be the same dimensions before we use them? how can i create a confusion matrix?
Thankyou so much for the explanation but I need to train a model for my face recognition project can you please guide how do I train the model for face recognition on both RGB and grey channel. And how can I structure my dataset either multiple folders of people or else?
should v need put images in all folders? like testing - in happy 5 images and unhappy 5 images? same for validation too? but high no. of images in training
i am reading a research paper on the visual with CNN. The size of the images is 250*500. The paper used CNN with 100 kernels of size 10*20 for 1st conv layer and 100 kernels of size 20*30 for 2nd layer. Can you expain abit of this as it is not clear to me why the chosen size..what is the effect of the huge filter size
sir I did everything like as u have said and I have trained the model but I am facing trouble during testing of a image. Can you kindly help me how should I make the model predict the image class?
if you liked the content please support by subscribing 😇
1. here is the video for multiclass:---- ruclips.net/video/1Gbcp66yYX4/видео.html
2. here is video for object detection with tensorflow:----- ruclips.net/video/_TCUPl3j2kI/видео.html
3. here is video for object detection with YoloV3:------ ruclips.net/video/zm9h4mYymk0/видео.html
Great tutorial!!! thanks. Here, I noticed you didn't normalize your test data, don't you think this might have had a negative impact on your prediction in some way? Since your model was trained and evaluated on normalized data. Although at 1st glance it doesn't seem so.
Hello sir, How to upload only one data set folder like chech happy or not
no need to check the saad, just happy folder so what channges i have to make in code
i need to check weather this is a plant leaf or not for my semester project so it will alot of help if you tell the code for single data set that the given image is the same or not in testing
Bro please give the code lines link
Hi, we use the same pictures in training and validation? or we use diferent?
This is the exact tutorial I am looking for. Thank you very much. You described all the steps in the most simplified way. This tutorial will help me a lot in my project so thank you again.
This is most awesome and most humble tutorial I've ever seen. Despite many other tuts that more like "watch me code" and throwing a line of code with complex variable naming to show off. Thank you.
Crystal clear implementation of CNN
oh god, i spent HOURS trying to figure out my errors. you helped in five minutes!
You know, here in Brazil us IT people praise IT people from your region.
After being stuck a whole day, I prayed for wisdom and bumped into your video. You are an answered prayer. Very grateful for your content. Keep at it. #NewSub
You are so welcome
🎯 Key points for quick navigation:
00:14 *Train a neural network to classify mood (happy/not happy) using custom images from Google Photos.*
01:34 *Organize images into training, testing, and validation folders for efficient model training.*
05:31 *Use `ImageDataGenerator` in TensorFlow to preprocess and label images automatically.*
09:00 *Design a convolutional neural network (CNN) for image classification, including convolutional and max pooling layers.*
11:19 *Compile and train the model using binary cross-entropy loss and RMSprop optimizer.*
13:55 *Achieve 100% accuracy on a small dataset; discuss implications and potential improvements.*
Made with HARPA AI
The best video ever for a person who studies deep learning and cnn ❤😍🔥
Thank You bro. After building 3 models I forgot the most basic thing, prediction on single random image file. Your video solved my issue. Much love from my side.
Exactly what I was looking for. Wonderful video and well explained. Thank You ❤️❤️❤️
Excellent tutorial😍 can’t thank you enough!🙌🏻🔥
this is very helpful. I bet if you were picking sad and happy from pictures of friends, the error goes up because too much variation in the photos
Thanks, Man for explaining this in the easiest way🙌
Thank you much for the video!! i really enjoy it and helped me a lot to understand more about CNN
Thanks a lot , this is exactly what i was looking for. Great job man!
Legend, thanks for explaining. i am finally able to put everything i learned about this in practice thanks :)
hi brother i am confused . i need your help .this lab is important to me?
Sir I don't know how to express my feelings u are great ❤️❤️ keep going sir
lol... the Neural Network did a good job classifying whether you are happy or not because honestly, I couldn't even tell.
This is the best video that I have come so far. Thank you so much Sir!!
The first working tutorial!!! Thanks a lot
Excellent ji.Really very good explanation with real time image's 🎉🎉🎉
you are a wonderful human being
great job explaining it, you're a great teacher
always the low quality videos that are the best out there
Your video is very good. I found it extremely useful. Maybe you could rethink the tags for your video so that it shows up quickly in the search.
Superb...
No word for thanks and appraisal .
good keep it up
Amazing !! True life saviour. I was looking for exactly the same.
Thankyou so much, its really help me, i can use my own image and its awesome
Thank you so much for this video. Cannot appreciate enough!
Model is overfitting and you are happy that ist giving 100% accuracy. OMG
Thank you 👍🏻🎉 for easy tutorial of CNn
Great bro ...!!! Very good explanation with appropriate pace ...!! Thank you bro !!
Glad you liked it!
wonderful tutorial. Thankyou so much. Just one request, Can you pls make a tutorial on how to evaluate this model by confusion matrix,F1score etc?
The best video ever🙏
Excellent I just finished it and it recognized most of my images (maybe could it have recognized everyone if I had used more images for training), thanks a lot.
there's no "basedata/test" folder isnt it? how you can finished it?
🥰🥰 bhai maja agya thank you vmro
This is an excellent tutorial, thank you so much!
this is the best video ,cong2ln broo
very nice video, good job bro
Thank you 👍
this tutorial is really good. thank you so much
I really enjoyed. Thanks Sir!!!
Glad you enjoyed it!
Thanks a lot for the amazing video. I tried it out for healthy and diseased plants, it looks like it wrongly identified few. Should i put them back in training folder and re-run everything again? Please suggest.
i love you sir, you making it work. So much thanks!
so helpful.I'm glad Sir
Simply Superb. 🙏🙏
what did u put inside validation folder?
Thank you very much sir, you explained step by step. but I have problem in last step. how to accept sub directory path and select both folder data set. please reply me. Thank you.
Hey, I like your video a lot. However, at 4:30, how exactly did you call this image from your folder? I can't quite seem to figure it out as you didn't show exactly how you called it. Also, when I type in "img = image.load_img("basedata/train/happy/3.PNG"), it's telling me that the file isn't found and there is no such file, even though there is since I created it. Lastly, when I type in "plt.imshow(img)", it's telling me that the name 'img' is not defined. Please help...I'm following your video and this is throwing me off. Thanks
Amazing job! Thank you so much for that
waoooh ,this is amazing ,thank you brother
Very well explained and to the point
Thank you very much for this kind of good explanation!
You are welcome!
Very neat explanation, thanks for the video
Glad it was helpful!
Hello sir, it worked really well till model fitting.
For 'model_fit' step, it is throwing an error called "AttributeError: 'DirectoryIterator' object has no attribute '_assert_compile_was_called' "
Did you sort out the issue, and why this error was encountered?
Thx, this is what i looking for.
Sir, i want to know that which model the CNN model used in this video is inspired from?
Very useful and great job, thanks you so much
Sir, My final year project is related to Automated Fruit Ripeness Detection using Deep Learning model. There will e at least four fruits , we have to train model to classify fruit either Underripe, ripe or overripe. Can you please help me. Can I make a GUI we-based in Flask in Google Collab??? or I should make Web Flask app using VS code and then integrate my model at Google Collab? Which will be more convenient for me?
One more thing in your video there are two cases(happy or not happy) and you kept it binary. Is there any ternary option available for three classes? underripe, ripe and overipe?
Pls do a tutorial for using and training datasets for Mask RCNN as well, your videos helped alot
Can you make leaf disease detection using CNN,keras, DeepLearning
Thank you very much. You made my day .I am happy to learn. Sir please upload more videos. Can you please send me code for model evaluation for same program
Yes, sure
Hi, excellent tut, but I want to ask a stupid question, do I need to train or test the network using the same person's face photo? thanks
Please make videos like this more
Sir, I got question regarding where did you save your photos initially? Per my understanding, you have put them into training folder. However, down the video, "there's 11 images in our validation dataset", you're saying at 8:40... I am confused: should I copy images to validation folder, too?
una parte de las imagenes van en el entrenamiento y otra en validacion, yo usaria 75
25 para cada carpeta
Thanks a lot, pls can this work with multi-class classification
Very helpfull tutorial. I have some questions though. Shouldnt all the images of the dataset be the same dimensions before we use them? how can i create a confusion matrix?
Excellent video thanks alot.
Supperb 👍
Kindly extend to check accuracy on test dataset
Hello nice video..:)
2 questions:
1. Since you have 19 unhappy photos how does batch(3) work here?
2. Diff. btw batch_size and steps per epoch?
Nice video! thanks man!
please do tutorial of this with tarfile. i have tar file image dataset and having trouble opening it
thankyou very much sir for the great demo,
but have you the video to explain the details of the models that we have to use for every scenario?
Yes, soon
Thankyou so much for the explanation but I need to train a model for my face recognition project can you please guide how do I train the model for face recognition on both RGB and grey channel. And how can I structure my dataset either multiple folders of people or else?
should v need put images in all folders? like testing - in happy 5 images and unhappy 5 images? same for validation too? but high no. of images in training
Ty for this video, you help me a lot rn.
Thank you for your valuable information sir
Thanks and welcome
it really helps thank you so much
Glad to hear that!
A very nice and informative video sir. Thank yoU !!
So nice of you
Awesome content
Good Job
i am reading a research paper on the visual with CNN. The size of the images is 250*500. The paper used CNN with 100 kernels of size 10*20 for 1st conv layer and 100 kernels of size 20*30 for 2nd layer. Can you expain abit of this as it is not clear to me why the chosen size..what is the effect of the huge filter size
thanks, this helped me!
Thanks bro, really helped
Hello, This helped me a lot but One question what chances would you make if we introduced a third output lets say neutral.
Thanks
thanks for your video ,
How to save this model
Amazing,thank you very much
Great, Jay
Very interesting video, helped me a lot !
working well, Thank a lot
Glad to hear that
Need help with
ValueError: logits and labels must have the same shape ((None, 512) vs (None, 1))
sir do we have to sotre photos in all the three folders like validation training and testing or only training
Thanks! Very useful
sir I did everything like as u have said and I have trained the model but I am facing trouble during testing of a image. Can you kindly help me how should I make the model predict the image class?
Can we know what model architecture you use in this model?
Or this is just a arxhitecture that u made by yourself?
Thankyou so much 😍😍
very useful! thank u so much ;)
Nice tutorial
can mediapipe will provide the accurate results with the guidance of this code? Please provide your Github link...
thanks a lot for your help