Image Classification Results
Config & Metrics
Train/Validation Plots
Test Plots
Training Setup
| Parameter | Value |
|---|---|
| Model Name | resnet18 |
| Epochs | Because of early stopping: the trainingstopped at epoch 7 |
| Batch Size | Auto-selected batch size by Ludwig: 16 |
| Fine Tune | True |
| Use Pretrained | True |
| Learning Rate | Auto-selected learning rate by Ludwig: 1e-05 Based on model architecture and training setup (e.g., fine-tuning). See Ludwig Trainer Parameters for details. |
| Random Seedearly Stop | N/A |
| Data Split | Detected a split column (with values 0 and 2) in the input CSV. Used this column as a base andreassigned 15.0% of the training set (originally labeled 0) to validation (labeled 1). |
Model trained using Ludwig.
If want to learn more about Ludwig default settings,please check the their website(ludwig.ai).
Model Performance Summary
| Metric | Train | Validation | Test |
|---|---|---|---|
| accuracy | 0.8417 | 0.1500 | 0.2000 |
| accuracy_micro | 0.8471 | 0.2000 | 0.2000 |
| hits_at_k | 0.9250 | 0.4500 | 0.3000 |
| loss | 0.6749 | 2.7907 | 2.8261 |
| roc_auc | 0.9998 | 0.7824 | 0.6917 |
Training & Validation Visualizations
Learning Curves Label Accuracy
Learning Curves Label Hits At K
Learning Curves Label Loss
Test Visualizations
Compare Classifiers Multiclass Multimetric Label Best10
Compare Classifiers Multiclass Multimetric Label Sorted
Compare Classifiers Multiclass Multimetric Label Top10
Compare Classifiers Multiclass Multimetric Label Worst10
Compare Classifiers Performance From Prob
Compare Performance Label
Confusion Matrix Label Top10
Confusion Matrix Entropy Label Top10
Frequency Vs F1 Label
Roc Curves
