| Trained with 1 GPU H800 Server from AutoDL on 2025.2.3 BJS with Pytroch and converted to .h5 format at the same time. | |
| Basic Model uses CNN with accuracy of 75% on test data (80.7 MB) | |
| V1 Engine uses CNN with accuracy of 87% on test data (72.1 MB) | |
| V2 Engine uses ViT with accuracy of at most 40% Keyboard Interrupted 2025.2.3 15:57:37 BJS | |
| V3 Engine uses Hybrid Model( Combination of Convolutional layers and a Multi-Layer Perceptron (MLP)) with accuracy 68.65% on test data. (34.3 MB) | |
| Trained 2025.2.4 BJS with H800 | |
| V4 Engine based of V1 but improve with: More Convolutional Layers. | |
| Bottleneck Blocks: We can use bottleneck blocks (1x1 conv before and after 3x3 conv) to reduce computation, and increase depth. | |
| Residual Connections: Implement residual connections to ease training in the very deep network and to help avoid vanishing gradients. | |
| Increased Filters: Use more filters in the layers to increase the learning capacity. | |
| Accuracy 89.39% on test data. | |