Lovely tutorial, thank you! 👍🏽

Thanks for helping me with my thesis !

I was working on a CNN for image classification but I couldn't wrap my head around after training the model, how to actually get the specific image classification on a random image from online. I was stuck on this issue for hours until your step-by-step video explained it perfectly and I finally got my model to output image labels after classifying. Thank you so much for this video!

@adarshpotteti8559

10 ай бұрын

Plz send me the code

@Khubaiblj

8 ай бұрын

Do u have the video m

Very well explained the use of keras library.

Awesome tutorial

Clearly explained!

Great tutorial!

Hello! Thanks for the tutorial, it's really cool. Nevermind, fixed it hahaha Thanks!

Best-One!! Amazing :)

great educator

先赞后看！！！thank you!!

sir thank u for this tutirial but would u do a video how to create your own model classifier by adding your own images

Thank you. If the dataset isn't inside of Keras, in other words, if my dataset is in my local drive how can I import the training_images?

@brucedickie9394

Жыл бұрын

from keras.datasets import cifar10

I think this was an EXCELLENT tutorial and I definitely will use the treasures that you gave on this difficult topic! I wish you had gone over the data that you were using I little bit more in detail and stated how you have to adjust the convoluted2D layers, because of the size difference of the images. In your example you used 32 pixel x 32 pixel Cifar10 dataset, but how does this change, if your input_shape is more complex ie. trying to classify whether there is an apartment building OR a detached house in a certain address => even my thumbnails that I am trying to classify are more complex 400 pixels x 400 pixels pictures and I do not want to make them smaller, because I am afraid that I would lose some features that make them more recognizable (although a very quick rule of a thumb would be the amount of asphalt and grass; lot of asphalt > an apartment building, lot of grass a detached house with its own garden). So evidently I am increasing my input_shape to match the size of my images, but how does that reflect the size of the convoluted 2D layers, do I need one that is exactly the size of my image 400 and three that are twice as large 800?

Is there a way to add an anomaly case to such classifications?

Great video but how do I label the images if i don't know the order of images? How to identify the label name from label number value?

Awesome tutorial, you explains so awesome, can you make a tutorial to create a chatbot using nlp

@NeuralNine

4 жыл бұрын

Its on my list! Thank you! :)

@pranaytiwari9204

4 жыл бұрын

@@NeuralNine i will wait.🙏🙏😊😊

@denethrangana7419

Жыл бұрын

@@NeuralNine hello dear friend Can i use camera to identify the object in thiscode.but the predict() function cant work within a while loop

i hope you do the same for text, for example try to check if a book translated Ok by raining the model on good translation of the same book. Kind of correcting the second translation

How can u extract the label of a classified image when working with a large dataset? I would like to classify food image and recommend recipes based on the label identified but I am not understanding how to do this. I WOULD kindly ask a tutorial on this matter. There is barely information on food related projects plz

lovely!

Thanks. Your videos, topics are relevant, your teaching style is great. But please dont torture your viewers, with this low contrast and small text !

Perfect

can you do a video that you show to cam a picture and soft reconize that picture tell you all you need to know of the thing in picture?

this is really quality video and enhance our skills too.

@ajarivas72

Жыл бұрын

It is an awesome video.

thank you for this tutorial, really helpful but i was wondering how long does it take to run this code?

I used Open Cv's resize() function to resize any image i need for me since when I tried resizing manually i got black borders around the image which caused problems. Not only did this fix that issue it also helped save me time. img = cv.resize(img, (32, 32)) img = cv.cvtColor(img, cv.COLOR_BGR2RGB) super easy

please when i give it an image of a another thing like a flower it classifies it is it normal how to correct it

Activate main didn't work. It's not found , what should i do? C:\WINDOWS\system32>activate main Could not find conda environment: main You can list all discoverable environments with `conda info --envs`.

cool stuff

can you tell me how to do like this with python: detect an image on an application and if the image is not there search for the image again and if it's there then do something.

Sir can we see something like NN in native python IDKY but making this in tensorflow doesnt satisfy me IDKY

Hello sir actually I wanted to know that is there any another method for pattern recognition from image except cnn , if yes then please tell me how many algorithms are there?

what do i need to do to make it read notes written by people and be able to classify them to which one is who's? please help

5:55 horse and truck appear similar XD

Mans is Nacho from Better Call Saul. Seriously though good video.

@NeuralNine

2 жыл бұрын

Thank you L

How do i use my own images for training the model?

@xavxavierm

3 жыл бұрын

yea

@RawCuriosity

Жыл бұрын

@@brucedickie9394How do I do that.

@brucedickie9394

Жыл бұрын

@@RawCuriosity imported cv2 and numpy as np libraries, had all the images I wanted in a folder, added them to a list: pic = cv2.imread(os.path.join(path, img)) pic = cv2.cvtColor(pic, cv2.COLOR_BGR2RGB) pic = cv2.resize(pic, (80, 80)) image_list.append(pic) np.save("training_images", np.array(training_images))

@RawCuriosity

Жыл бұрын

@@brucedickie9394 Tysm

@ajarivas72

Жыл бұрын

@@brucedickie9394 Thanks a lot for the lines of code. Very useful. How would you proceed regarding the training labels?

Hi, i'm using a dataset that i downloaded from github. It has a .json archive with the labels and a file with the images but i dont know how to import it to my python project. Someone can help me pls?

what version are you using my project is gettin errros

Great video! Would be nice to know, how to automate the scaling part in python somehow

@itunsicherheit

Жыл бұрын

I've done it with opencv: img = cv.imread(path, cv.IMREAD_UNCHANGED) resized = cv.resize(img, (32, 32), interpolation = cv.INTER_AREA)

Have you applied Alex Net Model ??

Question: Did you make any changes to your code before training your model? The output at the bottom of your screen at 31:52 in the video is very different from mine. I'm also getting: loss: 14.550795555114746 accuracy: 0.09724999964237213 What is your setup? Which OS are you using?

@Nobody-qw4qm

Жыл бұрын

He's using Windows OS And his setup in in the disc

Hey 👋 bud can you make an script for example i have thousands of images alright i.e mix photos of animals. But i need only 1 specific image so Image Recognized Software can find those img which is i want to copy once from my all images folder.

i think it would have helped if you had showed us all packages and their versions in your virtual env because i am getting a lot of dependency errors

How do I link it on my website?

Thanks for the great video. I was wondering how we can classify the color of a person (black, white, asian, etc) from a large set of pictures of humans? I

@linkstart1424

3 жыл бұрын

Great question. To answer, practically if you are trying to distinguish a human on say, a security camera. Though difficult, you could have it take a picture of the moments when a person is in view, using the neural network to detect people. Then process the images to only keep the person in the shot(difficult) then distinguish all the shades of color of the 'person' and provide a result based on what the program perceives as a 'Human color' and sifting out colors that likely are not like pink(Likely to mess up due to clothes color) Then pray, I mean pray it gets close. I'm very sorry but with the tech of today tech like that that can be considered even 50 percent accurate would be a dream, but alas. However, if this is for facial recognition then instead of one singular image of a car or plane, use thousands of pictures of people with "distinct" facial features, like Asian, or European. Then have separate neural networks entirely check to avoid the error: Is black or white. (White) Is European or Asian. Etc. This could work fairly well, but with apparent limitations like needing the image to only include the user's face, like to the degree that you may see on a license. All I can say is if you can somehow make this. You are a brilliant mind on par with Thomas Edison or Albert Einstein. Jokes aside though it was a great question, and I do hope you the honest best on your project. Regards, Link-Start.

Thank you! where can i find the full code of it?

@adamtoth7679

11 ай бұрын

import cv2 as cv import numpy as np import matplotlib.pyplot as plt from tensorflow.keras import datasets, layers, models (training_images, training_labels), (testing_images, testing_labels) = datasets.cifar10.load_data() training_images, testing_images = training_images / 255, testing_images / 255 class_names = ['Plane', 'Car', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck'] for i in range(16): plt.subplot(4,4,i+1) plt.xticks([]) plt.yticks([]) plt.imshow(training_images[i], cmap=plt.cm.binary) plt.xlabel(class_names[training_labels[i][0]]) training_images = training_images[:20000] training_labels = training_labels[:20000] testing_images = testing_images[:4000] testing_labels = testing_labels[:4000] model = models.Sequential() model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3))) model.add(layers.MaxPooling2D((2,2))) model.add(layers.Conv2D(64, (3,3), activation='relu')) model.add(layers.MaxPooling2D((2,2))) model.add(layers.Conv2D(64, (3,3), activation='relu')) model.add(layers.Flatten()) model.add(layers.Dense(64, activation='relu')) model.add(layers.Dense(10, activation='softmax')) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(training_images, training_labels, epochs=10, validation_data=(testing_images, testing_labels)) loss, accuracy = model.evaluate(testing_images, testing_labels) print(f'Loss: {loss}') print(f'Accuracy: {accuracy}') model.save('image_classifier.model')

Молодець!

When training the model i get this: WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading. Can someone help me?

@pesk8801

11 ай бұрын

just a warning. wont be fatal to the code, you will have to wait for tensorflow to fix it if you dont want to see the warning.

Can i get flowchart of this program... Please !! Fast

Hi, first of all, great tutorial! When I'm Training my Model, I only get 1563 'images', when using 'training_images = training_images[:20000]', to scale it down, it gets scaled down to around 600 images. Did TensorFlow change the size of the training models? And why are almost 100 images being removed when 'cutting' the array?

@al.d9592

2 жыл бұрын

Same here, its 625, which is exactly 1/32th of 20k. trying to figure out why...

@ormstoodotcom7634

Жыл бұрын

@@al.d9592 Any luck? 10 epochs of 625...

@benjaminwendel9175

Жыл бұрын

@@al.d9592 could you figure it out?

@benjaminwendel9175

Жыл бұрын

@@al.d9592 you have to specify the batch_size: model.fit(training_images, training_labels, batch_size = 1, epochs = ......... otherwise, the default batch_size will be taken, so 32 ... 20000 / 32 = 1563

@Ambient_Life

Жыл бұрын

@@benjaminwendel9175 yeah, the default batch size is kept 32, really helpful tip

Why when I train the model use only 1563 cases even if I pass the entire Vector of 50000 elements?

@benjaminwendel9175

Жыл бұрын

you have to specify the batch_size: model.fit(training_images, training_labels, batch_size = 1, epochs = ............... Otherwise, the default batch size will be 32, so 20000 / 32 = 1563. And you have to delete / comment out the part where we only take the first 20000 test images, otherwise it will still be limited to 20000 instead of 50000 images

how to correct the model when prediction is wrong?

Can i copy code plz?

This is great, but how can we do opposite, like feeding name to the trained model and it will generate a random image for us , just like MidJourney. Any suggestions?

can you zoom-in when you code

cifar10 not in datasets also i had to use from keras import datasets, layers, models becaus keras was not in tensorflow how to fix?

@proloshd5470

Жыл бұрын

import cv2 as cv import numpy as np import matplotlib.pyplot as plt from tensorflow.python.keras import layers, models from keras import datasets this worked for me

Does anyone know why we add dense layers

@mr_man5269

Жыл бұрын

They just help the AI get better results, basically it adds more variables that it can mess around with to get the outcome it wants

i'm getting an error like - File format not supported: filepath=image_classifier.model. Keras 3 only supports V3 `.keras` files and legacy H5 format files (`.h5` extension). Note that the legacy SavedModel format is not supported by `load_model()` in Keras 3. In order to reload a TensorFlow SavedModel as an inference-only layer in Keras 3, use `keras.layers.TFSMLayer(image_classifier.model, call_endpoint='serving_default')` (note that your `call_endpoint` might have a different name). Please help @NueralNine

@chiragbaid7275

15 күн бұрын

Bro i am getting the same error, u got its solution on how to resolve it ??

ValueError: `labels.shape` must equal `logits.shape` except for the last dimension. Received: labels.shape=(32,) and logits.shape=(512, 10) Getting this error when compiling the CNN model. Any Help?

Where is the source code he had use?

Nice lecture, thanks. Please how do I contact you?

plt.imshow is not working for me.. i get an error that I is out of range.. any suggestions plt.xlabel(class_names[training_labels[i][0]]) IndexError: list index out of range

@soundmartell

Жыл бұрын

Hello, I have the same issue, did you find a solution? Thanks

@mr_man5269

Жыл бұрын

Could you copy and paste your code?

@jamesgraymane3911

Жыл бұрын

you can try this plt.xlabel(class_names[list(training_labels[i])[0]])

im only on the 4th line and i have 4 errors and 6 warnings... anyone else having problems?

@CoderKai

Жыл бұрын

yes

@CoderKai

Жыл бұрын

'keras.datasets' has no attribute 'cifar10'

@_applejuice_

Жыл бұрын

@@CoderKai same

@_applejuice_

Жыл бұрын

need to replace the tensor flow import with these two lines: from tensorflow import keras from keras import datasets, layers, models

Sir can you please give me code for animal identification using ML algorithms and in Python code in crop protection

If I correctly understand that your test set is a subset of the training set, that’s a rookie mistake.

@pesk8801

11 ай бұрын

does it take too much from one classification? classifcations wont be balanced?

please source code

♥♥♥lets mix our code (if you know what I mean 😜)

It doesnt work. It just says "Deer", even when I predict with the first 16 test images. Bullshit.

I did not benifit anything in this video You just read what you are writting. You did not explain anythinh

The tutorial is fake. I get an error with the line plt.xticks([]) - TypeError: 'list' object is not callable. How can you call a list???

@-caio-

Жыл бұрын

😐