dogs-vs-cats-classification-computer-vision

• Final model gives 94% accuracy in classificaiton of dogs and cats dataset

About the dataset

• This dataset is taken from kaggle
• I have trained the model by downloading the entire dataset, however trained with 1000 cats and 1000 dogs due to unsufficient computational resources

Preprocessing

• converted all the Images to square 200 x 200 images

Models used

• I have used one block VGG,two block VGG , three block VGG , plotted the graphs between loss,val_loss, accuracy,val_accuracy and figured out, at which epoch , the overfitting occurs

Data Augmentation

• Data Augmentation is one of the regression technique, and it has improved the accuracy to around 60%.

Dropout

• Dropout is also a regularization technique, however it gave a accuracy of only 48%

Final Model

• Final model used is VGG 16, using transfer learning , I have trained the model and achieved a accuracy of 96% in classification of dogs and cats .

Inference

• Using Sample Image, I have tested the model and the Final Model predicted correctly