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Image Classification

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Image recognition implementation with Keras. A CNN is built and trained with the CIFAR-10 dataset. Two models are trained: one without data-augmentation (77.25% accuracy) and the other with data-augmentation (78.04% accuracy). Process:

image_recognition.py

  • Data processing: one-hot encoding and scaling
  • Building and training the CNN
  • Training the model
  • Training process evaluation
  • Evaluation of the model
  • Saving the trained model

my_image_recognition.py

  • Loading the trained model
  • Predicting on the test set
  • Evaluation of the predictions
  • Predicting on my own images

Followed Course

Predicting on my own images

Some are correct ✔️ some are not ❌

            
            
            
            
            
            

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Image classification with machine learning

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