Implementing a Convolutional Neural Network for Image Classification with PyTorch Using EuroSAT Data

Welcome to Dr. Azad Rasul's tutorial on implementing a Convolutional Neural Network (CNN) for image classification using PyTorch and the EuroSAT dataset! 📊📸
In this video, we will walk through:
Setting up the environment
Loading and transforming the EuroSAT dataset
Building and training a CNN model using PyTorch
Evaluating model performance and visualizing results
Key Topics Covered:
Setting Seed for Reproducibility: Learn how to ensure your results are consistent across different runs by setting a seed for random number generators.
Checking GPU Availability: Discover how to leverage GPU for faster training in PyTorch and verify if your setup supports it.
Data Preparation: Understand how to load and transform the EuroSAT dataset for training and testing.
Model Building: Follow along as we build a CNN model using a pre-trained ResNet50 architecture.
Training the Model: Learn the process of training the model, including defining the loss function and optimizer.
Model Evaluation: Evaluate the performance of the trained model using various metrics and visualize the results with a confusion matrix.
Sample Predictions: See the model in action by making predictions on sample images from the dataset.
Whether you are a beginner or an experienced practitioner, this video provides a comprehensive guide to implementing CNNs for image classification tasks using PyTorch.
Resources:
Code Repository: github.com/Aza...
EuroSAT Dataset: madm.dfki.de/fi...
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Thank you for watching, and happy coding!

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Code Repository: github.com/Azad77/LULC_CNN_EuroSAT/blob/main/CNNs_LULC_Classification_EuroSAT_Azad_Rasul.ipynb