| # Model Training Notes | |
| ## Validation Accuracy: `train0` | |
| *Note: "v1" = IMAGENET1K_V1, "v2" = V2* | |
| | Model | Run 0 | | |
| |---------------|----------| | |
| | ResNet50 v1 | 0.764273 | | |
| | ResNet50 v2 | 0.729282 | | |
| | ResNet101 v1 | 0.775936 | | |
| | ResNet101 v2 | 0.790055 | | |
| --- | |
| ## Validation Accuracy: `train1` | |
| *Utilizes new labeled test set from Stanford Cars for more training data!* | |
| | Model | Run 0 | | |
| |---------------|----------| | |
| | ResNet50 v1 | 0.848023 | | |
| | ResNet50 v2 | 0.833607 | | |
| | ResNet101 v1 | **0.867381** | | |
| | ResNet101 v2 | 0.861614 | | |
| --- | |
| ## Hyperparameterization: ResNet101v1 (`train1` best model) | |
| *Hyperparameters changed: optimizer and learning rate* | |
| | Description | Run 0 | | |
| |----------------|-----------| | |
| | Adam, lr=1e-4 | **0.867381** (baseline) ⭐ | | |
| | Adam, lr=3e-4 | 0.717875 | | |
| | Adam, lr=5e-5 | 0.841050 | | |
| | SGD, lr=1e-2 | 0.691104 | | |
| | SGD, lr=5e-3 | 0.417627 | | |
| --- | |
| ## Observations & Conclusions | |
| - **More data improves accuracy:** All models saw substantial gains in `train1` compared to `train0`. | |
| - **Deeper models help:** ResNet101 generally outperforms ResNet50. | |
| - **Optimizer matters:** Adam (`lr=1e-4`) yielded the highest accuracy; both lower/higher learning rates and SGD performed worse. | |
| - **IMAGENET v1 vs v2:** The difference between v1 and v2 initializations is minor compared to the effect of data volume and model size. | |
| - **Performance margins:** The right optimizer and learning rate can more than double validation accuracy for the same architecture. | |