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First of all thank you for your efforts 😇 We need more such videos for Ann , rnn, LSTM , transformers .. etc Hit like to if you looking for this videos 👍
Congratulations for the good and simple explanation, easy to read code. I encountered issues following another tutorial online for this dogs and cats project but found your video much easier to follow. I noticed in the end of training, your model has the accuracy of 77%. Mine is almost like yours, with different parameters, it reached 85% accuracy, still I'm not happy with it because I gave it 3 new photos as test, of 3 cats, he identified 2 as dogs (wrong) and only 1 right, as cat. I'm trying Inception now, still training, takes longer than your model but looks promising, on Epoch 5 of 10, accuracy is already 90%, let's see what we can get.
Thanks a lot brother it has taught me a lot and some concept which were not clear are now clear. It will be good if you share your code and dataset link so that we can too make our neural network.
@@aravindswamy7631 I tried with the plant this with rice disease and Healthy leaf classification. The first time it worked but then it did not work. Both the time, program run successfully but second time it could not classify the image.
Thank you bro you are predicting two class (dog/cat) right so i think in your neural network must have the parameter set as 2 in the last layer DENSE layer
Lovely tutorial! I have 1 question: instead of specifying the number of train samples (1000 in the tutorial) is there a way to use as train samples all of the images inside the data folder?
what would be the class mode for non-binary classification?like i have 5 categories to predict from ,what would be the class mode in that case? and how can i write
Can we use this classification model for Object detection. Can you please make a video on How will we do Object detection (Human) in images and videos using Tensorflow CNN from scratch. I want to make my own model but even on tensorflow website they are using pre-trained model for object detection.
Hello Sir nice explanation. I want to know one thing when you limit validation data to 200. it will pick 200 from cats and 200 from dogs separately. Thank you
honestly i cant find enough word to thank you Keep the great work up and do your best bro Thank you so much i have i a question if i want to can prediction function using python wpf application after this training how can that be possible
Superb, nicely explained every bit of the CCN two-class classification. One question, once we had saved the model .h5. now, how can I call the model for further prediction? Suppose I am calling this model using remote machine, so connect it with socket, and server code will call your model.h5.
can you help me in modelling a CNN architecture which will take two inputs separately and also provides the feature map separately as output using Functional API model?
How many number of neurons are there in Input layer, Conv2D layer 1 and 2 and dense layer? And Is the model taking 1 Image at a time or 20 images(batch size) in the CNN model ?
Thank you for your explanations, helps a lot, I'm facing a problem, I would like to determine wheter a person has long, mid, short hair, my train dataset has 1000 images (300each classes), my test dataset has 300 images (100each classes), the result of the prediction is always [[1. 0. 0.]] so always long ... my model is getting 87% accuracy and 0.2 loss and validation accuracy is 81% and validation loss 0.5. Could you help me to understand why I'm I getting the same results ?
May I know how to upload folder with folders to colab sir..I want to upload iris eye database with images directly taken and each folder has its variations also
thanx bro it did help me....but can you tell me how this prediction model can work for more than 2 outputs.(result=model.predict(img_pred) print (result) if result[0][0]==1: prediction = "cat" else: prediction = "dog" print (prediction)
why do you add the validation_generator while fitting your model ? should we just fit it with the train generator and then use model.evaluate(validation_data)??
Thanks for this video. I tried the same code and I got the following error . ValueError: Error when checking input: expected conv2d_8_input to have shape (150, 150, 3) but got array with shape (299, 296, 3) Any help would be greatly appreciated.
Nice explanation...bro can you help me with one doubt? If suppose in a image there is a cat and dog, I want to display the count of the dogs and cats in the image and also predict that cat and dog both are there in the image? How can I do it?
@@justinmathew5776 it will choose either dog or cat depending on maximum features of cat or dog matched to the trained parameters. I don't know the right answer.
Okay here is it how it works in 'binary classification' with 'sigmoid' activation, the output of the network will be a single scaler between 0 and 1 encoding the probability that the current predicting image is class 1(as opposed to class 0) higher the probability higher chance of the image to be type class 1 not class 0 therefore in this case cat is class 0 and dogs is class 1. Hence the predict function encodes the probability that the current image is class 1 which is dog. therefore if the probability of the current image is 1 then it is type class 1, in this case dog and if probability 0 then class 0 cat. The main thing is that the predict function encodes the probability that the current predicting image is of type class 1.And talking about why cat is class 0 and dog is class 1 the directory is labelled like this since cat is the first directory then dog, Hope it helps
what should be the prediction code for multi-class classification? my input shape is (150, 150, 3).. Input 0 of layer sequential_1 is incompatible with the layer: expected axis -1 of input shape to have value 1 but received input with shape (None, 150, 150, 3) this error always popping up
what if we give input data not as seperate dogs and cats? what happens or how to classify if the images are combined and we need to extract or seperate them from the combination of both of those images? please get back to me ASAP.
hey thanks for helping me understanding image classification. I got a problem with my code on the result. I tried the cat image but predicted as dog. I dont know whats went wrong. Help mee
