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Model 1000 Train 1000 Test 1000 F1 5000 Train 5000 Test 5000 F1 10000 Train 10000 Test 10000 F1
ResNet50 0.88 0.64 0.61 0.88 0.72 0.71 0.93 0.88 0.87
VisionTransformer 0.91 0.68 0.60 0.92 0.81 0.79 0.94 0.90 0.87
InceptionV3 0.90 0.71 0.65 0.88 0.75 0.67 0.92 0.89 0.88
GoogLeNet 0.91 0.68 0.68 0.77 0.71 0.72 0.85 0.83 0.82
DenseNet 0.98 0.67 0.64 0.93 0.72 0.70 0.89 0.84 0.82
ResNeXt50 0.99 0.62 0.61 0.97 0.76 0.72 0.94 0.89 0.88
ELA + ResNeXT (Ours) 0.92 0.75 0.69 0.93 0.83 0.82 0.98 0.94 0.93

Results for the models: ResNet50, Vision Transformer, InceptionV3, GoogLeNet, DenseNet, ResNeXt50. Run all the blocks of "classifier.ipynb" notebook for train and test accuracy.

Results for the model "ELA + ResNeXT": Run the "data.ipynb" notebook first to preprocess the raw RGB images to generate corresponding error level analysis of the image. Then run all the blocks of "ELA_ResNeXt_Classifier.ipynb" for train and test accuracy.

Run the "plots.ipynb" notebook for Train Accuracy plot and confusion matrix of the best performing model.

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