| 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.
