Plant Disease Detection System Part-5 | Saving and Evaluating Model Performance
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 Visualization of the Model on the training and validation set
Later in the upcoming video of this playlist, we will compute Precision, Recall, f1-score, and Confusion Matrix for this model
#imageclassification #tensorflow #deeplearning #imageprocessing #python
#machinelearning #convolutionalneuralnetworks #trainingmodel #overfitting #datavisualization #accuracy #validation #json
#endtoendmachinelearningproject #datascienceprojects
Playlist Link: • Plant Disease Detection System Introductio...
Keras Model Evaluation:
https://keras.io/api/models/model_tra...
Tensorflow Model Saving and Loading:
https://www.tensorflow.org/tutorials/...
Project Link: https://github.com/animesh1012/machin...
Trained Model Link (keras file): https://drive.google.com/file/d/1OK9j...
Dataset Link:
https://www.kaggle.com/datasets/vipoo...
Entire Project Code:
https://drive.google.com/drive/folder...
Music Genre Classification System Part-12 | Performing Model Prediction on New Audio File
Plant Disease Detection System Part-7 | Building Confusion Matrix for Deep Learning Model