experiment_info dict | training_config dict | data_info dict | final_metrics dict | best_metrics dict | training_history dict |
|---|---|---|---|---|---|
{
"model": "efficientnet_b3",
"timestamp": "20251109_165427",
"kaggle_kernel": "Interactive",
"device": "cuda"
} | {
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"img_size": 256,
"age_weight": 0.3,
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"optimizer": "AdamW",
"scheduler": "CosineAnnealingWarmRestarts",
"loss_functions": {
"age": "SmoothL1Loss",
"gender": "CrossEntropyLoss (label_smoothi... | {
"total_images": 24708,
"train_split": 0.85,
"val_split": 0.15,
"augmentations": [
"RandomHorizontalFlip",
"RandomRotation(15)",
"RandomAffine",
"ColorJitter",
"RandomGrayscale",
"RandomErasing"
]
} | {
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"val_age_mae": 0.0882152318954467,
"val_gender_f1": 0.9419918060302734,
"train_loss": 0.2419542074203491,
"train_age_rmse": 0.1235168799757957,
"train_gender_acc": 0.9804770946502686
} | {
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"best_val_age_rmse": 0.1161882653832435,
"best_val_gender_f1": 0.9458882212638856,
"best_epoch": 18
} | {
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... |
{
"model": "efficientnet_b3",
"timestamp": "20251110_181006",
"kaggle_kernel": "Interactive",
"device": "cuda"
} | {
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"img_size": 256,
"age_weight": 0.3,
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"optimizer": "AdamW",
"scheduler": "CosineAnnealingWarmRestarts",
"loss_functions": {
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"gender": "CrossEntropyLoss (label_smoothi... | {
"total_images": 24708,
"train_split": 0.85,
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"augmentations": [
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"RandomRotation(15)",
"RandomAffine",
"ColorJitter",
"RandomGrayscale",
"RandomErasing"
]
} | {
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} | {
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],
"val_loss_history": [
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0.2980527877807617,
0.2825332581996918,
0.2817873358726501,
0.2797755002975... |
{
"model": "efficientnet_b3",
"timestamp": "20251111_031138",
"kaggle_kernel": "Interactive",
"device": "cuda"
} | {
"batch_size": 16,
"learning_rate": 0.0002,
"max_epochs": 40,
"epochs_trained": 34,
"img_size": 256,
"age_weight": 0.3,
"gender_weight": 1,
"optimizer": "AdamW",
"scheduler": "CosineAnnealingWarmRestarts",
"loss_functions": {
"age": "SmoothL1Loss",
"gender": "CrossEntropyLoss (label_smoothi... | {
"total_images": 24708,
"train_split": 0.85,
"val_split": 0.15,
"augmentations": [
"RandomHorizontalFlip",
"RandomRotation(15)",
"RandomAffine",
"ColorJitter",
"RandomGrayscale",
"RandomErasing"
]
} | {
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"train_gender_acc": 0.9830960631370544
} | {
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"best_val_age_rmse": 0.1174851730465889,
"best_val_gender_f1": 0.9469081163406372,
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0.9676792621612548... |
{
"model": "efficientnet_b3",
"timestamp": "20251111_031508",
"kaggle_kernel": "Interactive",
"device": "cuda"
} | {
"batch_size": 16,
"learning_rate": 0.0002,
"max_epochs": 40,
"epochs_trained": 34,
"img_size": 256,
"age_weight": 0.3,
"gender_weight": 1,
"optimizer": "AdamW",
"scheduler": "CosineAnnealingWarmRestarts",
"loss_functions": {
"age": "SmoothL1Loss",
"gender": "CrossEntropyLoss (label_smoothi... | {
"total_images": 34708,
"train_split": 0.85,
"val_split": 0.15,
"augmentations": [
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"RandomRotation(15)",
"RandomAffine",
"ColorJitter",
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]
} | {
"val_loss": 0.2846643626689911,
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"val_age_mae": 0.0874540582299232,
"val_gender_f1": 0.9375288486480712,
"train_loss": 0.2355512529611587,
"train_age_rmse": 0.1220591515302658,
"train_gender_acc": 0.9830960631370544
} | {
"best_val_harmonic_mean": 0.9699426293373108,
"best_val_age_rmse": 0.1174851730465889,
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} | {
"val_harmonic_mean_history": [
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0.9645293951034546,
0.9675949811935424,
0.9680346846580504,
0.9649598598480223,
0.9676792621612548... |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Training Logs - EfficientNet-B3
Experiment: 20251111_031508
Model Architecture
- Base Model: EfficientNet-B3 (pretrained on ImageNet)
- Task: Multi-task learning (Age Regression + Gender Classification)
- Input Size: 256x256
Training Configuration
- Batch Size: 16
- Learning Rate: 0.0002
- Optimizer: AdamW (weight_decay=1e-4)
- Scheduler: CosineAnnealingWarmRestarts
- Epochs Trained: 34 / 40
Best Results
- Validation Harmonic Mean: 0.9699
- Validation Age RMSE: 0.1175 years
- Validation Gender F1: 0.9469
- Best Epoch: 12
Final Metrics (Last Epoch)
- Val Loss: 0.2847
- Val Harmonic Mean: 0.9650
- Val Age RMSE: 0.1177
- Val Age MAE: 0.0875
- Val Gender F1: 0.9375
Files in this Dataset
pytorch_lightning/metrics_20251111_031508.csv- Complete training historysummaries/training_summary_20251111_031508.json- Detailed metrics and configtrackio/trackio_logs_20251111_031508.zip- TrackIO logs (if available)
Next Steps
Use the best checkpoint for inference:
checkpoint_path = "checkpoints/efficientnet/effnet-epoch=12-val_harmonic_mean=0.9699.ckpt"
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