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SPOTLESS TECH
Индия
Добавлен 18 апр 2020
Hi folks
This is a Beginner Friendly channel where you will get contents in detail
You will get things related to Python, Machine Learning and some other amazing stuffs
Stay connected and keep Learning
This is a Beginner Friendly channel where you will get contents in detail
You will get things related to Python, Machine Learning and some other amazing stuffs
Stay connected and keep Learning
Music Genre Classification System Part-9 | Training Deep Learning Model using TensorFlow
Hello and Welcome guys
In this project, we'll learn how to make a powerful deep-learning model for 10 different classes of music audio
In this video, we'll train the deep learning audio classification model using TensorFlow.
We'll also improve the training speed of model architecture.
We'll discuss what steps we can take to boost training speed and model performance.
Later in the upcoming video of this playlist, we will tackle and implement the entire project step by step
#imageclassification #convolutionalneuralnetworks #audioclassification
#deeplearning #datascienceprojects #machinelearning #musicgenre #featureengineering #audiofeatureextraction #tensorflow #sklearn #modeltraining
Playlist Li...
In this project, we'll learn how to make a powerful deep-learning model for 10 different classes of music audio
In this video, we'll train the deep learning audio classification model using TensorFlow.
We'll also improve the training speed of model architecture.
We'll discuss what steps we can take to boost training speed and model performance.
Later in the upcoming video of this playlist, we will tackle and implement the entire project step by step
#imageclassification #convolutionalneuralnetworks #audioclassification
#deeplearning #datascienceprojects #machinelearning #musicgenre #featureengineering #audiofeatureextraction #tensorflow #sklearn #modeltraining
Playlist Li...
Просмотров: 44
Видео
Music Genre Classification System Part-8 | Building Model Architecture from scratch using TensorFlow
Просмотров 72День назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 10 different classes of music audio In this video, we'll build model architecture from scratch using TensorFlow We'll see a discussion on the functions and modules in detail which are used to develop deep learning model architecture Later in the upcoming video of this playlist, we will tackle and ...
Music Genre Classification System Part-7 | Preparing and Splitting data into training and test set
Просмотров 7414 дней назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 10 different classes of music audio In this video, we'll prepare our feature vectors and labels Also, we'll split features and labels into Training set and Test set. Later in the upcoming video of this playlist, we will tackle and implement the entire project step by step #imageclassification #con...
Music Genre Classification System Part-6 | Audio Data Preprocessing using Python Part-3
Просмотров 108Месяц назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 10 different classes of music audio In this video, we'll perform data preprocessing on complete dataset and develop a preprocess function which will take all 10 classes of audio file and return features and its corresponding labels This is Final Part of Audio Data Preprocessing Later in the upcomi...
Music Genre Classification System Part-5 | Audio Data Preprocessing using Python Part-2
Просмотров 88Месяц назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 10 different classes of music audio In this video, we'll build Melspectrogram for whole audio and chunks of audio and see the advantage of chunking audio This is Part 2 of Audio Data Preprocessing Later in the upcoming video of this playlist, we will tackle and implement the entire project step by...
Music Genre Classification System Part-4 | Audio Data Preprocessing using Python Part-1
Просмотров 1512 месяца назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 10 different classes of music audio In this video, we'll split audio into chunks and see the advantage of chunking audio This is Part 1 of Audio Data Preprocessing Later in the upcoming video of this playlist, we will tackle and implement the entire project step by step #imageclassification #convo...
Music Genre Classification System Part-3 | Loading and Visualising Audio file using Python Librosa
Просмотров 1772 месяца назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 10 different classes of music audio In this video, we'll see how we can load and do visualization of Audio files using Python Librosa Later in the upcoming video of this playlist, we will tackle and implement the entire project step by step #imageclassification #convolutionalneuralnetworks #audioc...
Music Genre Classification System Part-2 | Melspectrogram Tutorial with Python Librosa
Просмотров 2553 месяца назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 10 different classes of music audio In this video, we'll see the melspectrogram which is a audio feature extraction technique Later in the upcoming video of this playlist, we will tackle and implement the entire project step by step 00:00 Intro 02:17 What is Melspectrogram 07:05 Steps to create Me...
Music Genre Classification System Introduction | Audio Classification Project | Overview of Project
Просмотров 1,3 тыс.4 месяца назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 10 different classes of music audio In this video, we'll see the entire project at a high level Later in the upcoming video of this playlist, we will tackle and implement the entire project step by step #imageclassification #convolutionalneuralnetworks #audioclassification #deeplearning #datascien...
Plant Disease Detection System Part-9 | Building Web Application using Streamlit
Просмотров 17 тыс.5 месяцев назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 38 different classes of image In this video, we'll make plant disease recognition web application using streamlit. We'll use our plant disease recognition model which we created earlier to build web application Also we will see things in detail how we can make rapid UI of web application and integ...
Plant Disease Detection System Part-8 | Performing Model Prediction on Test Images
Просмотров 8 тыс.6 месяцев назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 38 different classes of image In this video, we'll learn how we can perform model prediction on new test images also we'll learn how can preprocess new images before feeding them to the model for prediction Later in the upcoming video of this playlist, we will build a web application for this proj...
Plant Disease Detection System Part-7 | Building Confusion Matrix for Deep Learning Model
Просмотров 4,9 тыс.6 месяцев назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 38 different classes of image In this video, we'll learn how we can calculate confusion matrix of convolution neural network deep learning model We'll see what is significance of confusion matrix We'll also learn to perform data visualization of confusion matrix using seaborn and matplotlib python...
Plant Disease Detection System Part-6 | Precision, Recall and F1 Score of Deep Learning Model
Просмотров 5 тыс.7 месяцев назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 38 different classes of image In this video, we'll learn how we can calculate precision, recall f1-score of deep learning convolution neural network model We'll see key difference between precision, recall and also what is confusion matrix Later in the upcoming video of this playlist, we will comp...
Plant Disease Detection System Part-5 | Saving and Evaluating Model Performance
Просмотров 7 тыс.7 месяцев назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 38 different classes of image In this video, we'll save the model we trained in the last video and evaluate our model performance on training and validation set data. We'll see some different ways of saving the deep learning model We'll Record History of our Model Also, we'll do an Accuracy Visual...
Plant Disease Detection System Part-4 | Training Image Recognition Model using Tensorflow
Просмотров 9 тыс.7 месяцев назад
Hello and Welcome guys In this project, we'll learn how to make a powerful deep-learning model for 38 different classes of image In this video, we'll train the deep learning model and we'll discuss in detail how to avoid the problem of underfitting, overfitting, and most importantly the problem of overshooting loss function Also, we'll improve our model performance by modifying the model archit...
Plant Disease Detection System Part-3 | Building Deep Learning Model Architecture using Tensorflow
Просмотров 10 тыс.7 месяцев назад
Plant Disease Detection System Part-3 | Building Deep Learning Model Architecture using Tensorflow
Plant Disease Detection System using Deep Learning Part-2 | Data Preprocessing using Keras API
Просмотров 26 тыс.7 месяцев назад
Plant Disease Detection System using Deep Learning Part-2 | Data Preprocessing using Keras API
Plant Disease Detection System Introduction | Image Classification Project | Overview of Project
Просмотров 60 тыс.7 месяцев назад
Plant Disease Detection System Introduction | Image Classification Project | Overview of Project
Amazon Redshift Tutorial for Beginners Part-3 | Data Loading and Unloading in Redshift #copycommand
Просмотров 959 месяцев назад
Amazon Redshift Tutorial for Beginners Part-3 | Data Loading and Unloading in Redshift #copycommand
Amazon Redshift Tutorial for Beginners Part-2 | Amazon Redshift System Architecture | AWS Redshift
Просмотров 729 месяцев назад
Amazon Redshift Tutorial for Beginners Part-2 | Amazon Redshift System Architecture | AWS Redshift
Amazon Redshift Tutorial for Beginners Part-1 | Data Warehouse vs Data Lake | AWS Data Warehouse
Просмотров 1229 месяцев назад
Amazon Redshift Tutorial for Beginners Part-1 | Data Warehouse vs Data Lake | AWS Data Warehouse
Building Web Application using Streamlit | Fruits and Vegetables Recognition System Part-10
Просмотров 6 тыс.11 месяцев назад
Building Web Application using Streamlit | Fruits and Vegetables Recognition System Part-10
Streamlit Tutorial | Deploying Web App using Streamlit | Basics of Streamlit
Просмотров 10 тыс.Год назад
Streamlit Tutorial | Deploying Web App using Streamlit | Basics of Streamlit
Fruits and Vegetables Recognition System Part-9 | Improving Deep Learning Model Performance
Просмотров 4,6 тыс.Год назад
Fruits and Vegetables Recognition System Part-9 | Improving Deep Learning Model Performance
Fruits and Vegetables Recognition System Part-8 | Image Prediction on Test set Part-2
Просмотров 5 тыс.Год назад
Fruits and Vegetables Recognition System Part-8 | Image Prediction on Test set Part-2
Fruits and Vegetables Recognition System Part-7 | Image Prediction on Test set Part-1
Просмотров 3,6 тыс.Год назад
Fruits and Vegetables Recognition System Part-7 | Image Prediction on Test set Part-1
Fruits and Vegetables Recognition System Part-6 | Training and Validation Accuracy Visualization
Просмотров 3,4 тыс.Год назад
Fruits and Vegetables Recognition System Part-6 | Training and Validation Accuracy Visualization
Fruits and Vegetables Recognition System Part-5 | Saving model and Recording history in json file
Просмотров 3,6 тыс.Год назад
Fruits and Vegetables Recognition System Part-5 | Saving model and Recording history in json file
Fruits and Vegetables Recognition System Part-4 | Training and Compiling Image Recognition Model
Просмотров 4,1 тыс.Год назад
Fruits and Vegetables Recognition System Part-4 | Training and Compiling Image Recognition Model
Fruits and Vegetables Recognition System Part-3 | Building Image Recognition Model using tensorflow
Просмотров 5 тыс.Год назад
Fruits and Vegetables Recognition System Part-3 | Building Image Recognition Model using tensorflow
I am getting predicted_categories output as <tf.Tensor: shape=(17572,), dtype=int64, numpy=array([0, 0, 0, ..., 0, 0, 0], dtype=int64)>
why date column not taking into analysis
In that data there is entry that whether it will rain on next day or not based on current day scenario and we are training model so that it can learn the feature of current day and based on that predict whether it will rain tomorrow or not
ValueError Traceback (most recent call last) Cell In[54], line 1 ----> 1 training_history = model.fit(x=training_set,validation_data=validation_set,epochs=10) File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\utils\traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs) 119 filtered_tb = _process_traceback_frames(e.__traceback__) 120 # To get the full stack trace, call: 121 # `keras.config.disable_traceback_filtering()` --> 122 raise e.with_traceback(filtered_tb) from None 123 finally: 124 del filtered_tb File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\backend\tensorflow n.py:554, in categorical_crossentropy(target, output, from_logits, axis) 548 raise ValueError( 549 "Arguments `target` and `output` must be at least rank 1. " 550 "Received: " 551 f"target.shape={target.shape}, output.shape={output.shape}" 552 ) 553 if len(target.shape) != len(output.shape): --> 554 raise ValueError( 555 "Arguments `target` and `output` must have the same rank " 556 "(ndim). Received: " 557 f"target.shape={target.shape}, output.shape={output.shape}" 558 ) 559 for e1, e2 in zip(target.shape, output.shape): 560 if e1 is not None and e2 is not None and e1 != e2: ValueError: Arguments `target` and `output` must have the same rank (ndim). Received: target.shape=(None,), output.shape=(None, 38) i am getting this error
Sir, is this applicable for fruit diseases like citrus?
Yes its applicable to fruits veg anytype of disease all you need to train your model with that type and its corresponding label
Sir i have done step by step way but the accuracy is coming ONLY 2%
Try using my notebook from github And let me know if any issue
Sir I have tried again using the same code as provided in GitHub . It took almost 5hr for all epoch to complete the training for model. But it gave an accuracy of 0.32.
I got the value error That the exception encountered when calling conv2D.call() Input=tf.tensor(shape(none,2,2,512),dtype=float32) What i will do??
What input shape you are using ?
@@SPOTLESSTECHas per your instruction I used the same input shape
Again the another error is showing that valuerror:'target' and 'output' must have same shape.Recived:target.shape=(None,20),output.shape=(None,38). What shall I do😢😢??
@@Supriyanayak21 In Output layer number of units (which is number of neuron) must be equal to number of classes present in dataset There is mismatch in neuron of last output layer check whether its equal to no of classes or not
I love you bro! Thanks for this masterpiece, your other videos are wonderful too. Much love broski
Thanks alot Keep learning 😊
Hello sir, Thank you for this playlist. I have created my model as well as my project. But there is one issue I'm facing with my model, that is, if I use test images from the dataset to check the model's prediction, it is showing correct results, but as soon as I use images from the internet to check model, it wrongly predicts them. Is this an issue of overfitting? or what? How can I resolve this issue?
Actually this problem is due to dataset Your model is trained on some specific type of images if you want your model accurate on internet image you have to mix 40-50% of internet images in each classes and retrain your model
please uplode next
Will upload soon 😊
Hello I am working in this project. Can I integrate autobill in it?
Yeah
Hello Bro, I can use this project as my Final Year Mtech project? Please guide me. Or which one's should i choose.
I am not familiar about the expectation of your faculties I would suggest you talk to them first then let me know
@@SPOTLESSTECH can you share research paper?
ValueError: Arguments `target` and `output` must have the same rank (ndim). Received: target.shape=(None, 38), output.shape=(None, 3, 3, 38) What should I do sir??
Are you using same dataset and following video step by step Check i have printed shape of training set before starting building model check and debug it
At 4:00 after runnign this command , i got a value error saying " ValueError: Arguments `target` and `output` must have the same shape. Received: target.shape=(None, 36), output.shape=(None, 7056) ". Please Help
Change number of units value in output layer equal to number of classes present in your dataset If you are using same dataset as in video then change it to 36
@@SPOTLESSTECH I got my error fixed by a very strange thing suggested by gemini , and that was to use cnn.compile (this line) with training history line and run them together , i dont know what was the reason behind it, but worked for me.
@@SPOTLESSTECH Thanks for replying man , i appreciate it
cnn = tf.keras.models.load_model(r"C:\Users\sutap\Downloads\trained_model (1).keras") their is an error please reply me error is valueerror ValueError Traceback (most recent call last) File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\ops\operation.py:234, in Operation.from_config(cls, config) 233 try: --> 234 return cls(**config) 235 except Exception as e: File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\layers\convolutional\conv2d.py:109, in Conv2D.__init__(self, filters, kernel_size, strides, padding, data_format, dilation_rate, groups, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs) 89 def __init__( 90 self, 91 filters, (...) 107 **kwargs
thank you for this playlist and where can i found the dataset of plants used for agriculture(like groundnut,turmeric,paddy,..etc)
Check on kaggle, uci machine learning repo, mit dataset these are some popular platform where you will get opensource dataset
Does this app work in the end
Yes are you having any issue just let me know
Hi please why was open cv not used for image preprocessing or has this been done before, also can VS code be used to build this system
Tensorflow is more powerful as compared to opencv for image feature extraction and it supports lots of modules Yes you can use vs code
Thank you very much for your reply. When using vs code do you just install the libraries and use the codes from the video tutorials there directly
need code for webapp and vedio for this project
You can build webapp using streamlit check my latest playlist you will get idea how to make deep learning web application
sir where is the webapp code and vedio for this project
Not developed webapp for that project Recording your feedback will plan to build some other rain prediction project with web application
Hi pls tell which algorithm is used in this project
Convolutional Neural Network Its a Deep Learning algorithm powerful on image related problems
My notification code is running successfully in python. I have created an exe file by importing the required packages. When i open that file after doing all settings the file is automatically closed before the notification comes.how to fix it sir please guide me.Thank You!
Looks like some libraries or dependencies are missing Usually this happens when pyinstaller not get all the required file and package to run application
@@SPOTLESSTECH No Module named 'Plyer.platforms' this message showing. How to install this package.
pip install plyer Install using this Add this to pyinstaller so that it can take it during bundling your application
sir i understood that you used CNN architecture, but is there any specific CNN type you are using
No here whatever architecture is developed its developed from scratch using tensorflow
@@SPOTLESSTECH thank you sir
sir i am unable to load the model in vs code which has the same environment as test.ipynb file
Try to load model using h5 file format You will find h5 and keras both file Change the code with h5 file location Then You will not face issue
Hello, thank you very much for this. But I'm having issue with my keras file, it keeps saying this when i hit the predict button "ValueError: File not found: filepath=trained_plant_disease_model.keras. Please ensure the file is an accessible `.keras` zip file."
In windows please use h5 file format model file to load model Please watch video i have saved in two format try with h5 you will not face issue
the markdown -- ## Importing Libraries.....is not working
its not important, and how does a markdown not work? ## --> space --> text --> shift+enter
Correct
Hello sir... I found a error on feature scaling I write exact same code I don't know why this is showing error
Dm the screenshot of error on instagram
sir can i add calorie details of the predicted fruit/vegitable by web scraping.
Yes you can
@@SPOTLESSTECH does this work with real world applications.if not what can i do to make it suitable for real world images
Try to mix real world images to each class and retrain your model to make it robust on all type of images
sir what is the architecture used?
CNN Architecture is made from scratch
Which Architecture is used while building the model????
I have build architecture from scratch using Convolutional Neural Networks
what are the frameworks you have used for this project
Tensorflow
hii sir, i am getting an error in cnn = tf.keras.models.load_model('trained_model.keras') in testing_plant_disease please give me a solution
What error you are getting dm me screenshot of error on instagram
PLEASE HELP ME ! what is the version of tensorflow? i have error like below ERROR: Cannot install protobuf==4.25.3 and streamlit==1.16.0 because these package versions have conflicting dependencies. ImportError: cannot import name 'builder' from 'google.protobuf.internal' (D:\test\BDPapayaLeaf\strlenv\lib\site-packages\google\protobuf\internal\__init__.py)
I am using in this project Tensorflow 2.15.0 Check 1st video of the playlist comment section I have pinned all the package with version
Sharing you all required libraries with there version tensorflow==2.15.0 scikit-learn==1.3.0 numpy==1.24.3 matplotlib==3.7.2 seaborn==0.13.0 pandas==2.1.0 streamlit==1.31.1
He'llo sir could u please share the synopsis of this
Sharing you all required libraries with there version tensorflow==2.15.0 scikit-learn==1.3.0 numpy==1.24.3 matplotlib==3.7.2 seaborn==0.13.0 pandas==2.1.0 streamlit==1.31.1 librosa==0.10.1
Is this project completed in the series i want to make it for for college major project
Yes its completed from my side UI part you can do by taking knowledge from my latest project series
can you mention the requirements.txt I mean all the versions used in this project
tensorflow==2.15.0 scikit-learn==1.3.0 numpy==1.24.3 matplotlib==3.7.2 seaborn==0.13.0 pandas==2.1.0 streamlit==1.31.1
Do we need same image both for test and validation dataset ? Here both validation and test dataset have same images .
Validation and test have different images to evaluate robustness of model
Hey in Test_music_genre after running the cell #Loading Model model = tf.keras.models.load_model("Trained_model.keras") I'm getting an error that says: AttributeError: 'Adam' object has no attribute 'build'. How to rectify it?
Try with tensorflow v2.15
Can you share/ upload the streamlit code in GitHub link?
Not build yet will make web app while recording for this series Then will upload
Is it complete project playlist
Yes its completed
Have you uploaded the streamlit web application? if not, when will you be uploading?
Not yet Lots of things are pending Will upload step by step
Your Playlist and demonstration is awesome ❤. Please keep it up. If possible please upload a video that how we can deploy this model in our mobile phone through android app. 🙏🙏
Okay thanks for your feedback
bro after compiling my accuracy is only 0.028 (very low) i'm confused pliz 🙏help!!!!!!!
Check your model architecture is it same as in video
i compiled again from the beginning and change the optimzer to 'adam' and works fine....thanks
sir i am getting "--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[59], line 1 ----> 1 class_name = valid_set.class_names 2 print(class_name) AttributeError: '_MapDataset' object has no attribute 'class_names'"
Which tensorflow version you are using ?
2.16 sir i had rectified the issue but had i manually inserted all classes into list
Hi your video's are very useful and good to learn things from scratch. We are more interested to learn AI projects from your channels please keep uploading more resources. Thanks again for your wonderful presentation and contents.
Thanks for your valuable feedback 👍👍
can i used google colab?
Yeah 👍
Hello. What model did you use? Like is it CNN or?
CNN model from scratch
If i click the predict button it is give me a recursion error what should i do
Something wrong in your code Do one thing test project using my code github link is in description Then debug and see where you are doing wrong
Thank you so much sir for this amazing tutorial But in my case I've got 100 % accuracy My data is totally right and there is no any NAN or missing data So is it worthwhile or there is a mistake somewhere?
You have to check training validation and test accuracy Sometimes what happened all good data lies in test set then u will get good accuracy so you have to split it in three parts training test validation and check all three Also check precision recall f1 score Confusion Matrix Then come to some conclusion
Thank you for your response I've finally got it around 72% Sir I'm civil engineering running student from Nepal and I'm doing a research work related to Flood forecasting with ML algorithms I need your help So could you please send me your email or anything so I can connect with U Thank you @@SPOTLESSTECH
hi is it cool if i use this for my final year project? and is it possible to get animal disease detection videos too?
is it cool with your professor(s)/instructor(s) ?
have you use machine learning in this project?
Both while training used deep learning For model evaluation used concepts of machine learning Machine learning is not limited to its algorithm its a superset Don’t get confused