| { | |
| "model_type": "ResidualConvAutoencoder", | |
| "architecture": "autoencoder", | |
| "latent_dim": 512, | |
| "image_size": 128, | |
| "input_channels": 3, | |
| "dropout": 0.1, | |
| "num_epochs_trained": 30, | |
| "best_epoch": 29, | |
| "batch_size": 1024, | |
| "learning_rate": 0.0001, | |
| "weight_decay": 1e-05, | |
| "gradient_clip": 1.0, | |
| "datasets": [ | |
| "CIFAR-10", | |
| "CIFAR-100", | |
| "STL-10-train", | |
| "STL-10-unlabeled" | |
| ], | |
| "best_val_loss": 0.00797, | |
| "training_time_minutes": 21.4, | |
| "framework": "pytorch", | |
| "task": "image-reconstruction", | |
| "separation_ratio": 19.18, | |
| "description": "Residual Convolutional Autoencoder trained on CIFAR-10, CIFAR-100, and STL-10 for deepfake detection via reconstruction error" | |
| } |