paligemma-architecture-styles

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paligemma-architecture-styles

This model is a fine-tuned version of google/paligemma2-3b-pt-448 on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_HF with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 20
• num_epochs: 3

Training results

TrainOutput(global_step=261, training_loss=1.761135561912681, metrics={'train_runtime': 1063.4627, 'train_samples_per_second': 1.975, 'train_steps_per_second': 0.245, 'total_flos': 3.156513684279552e+16, 'train_loss': 1.761135561912681, 'epoch': 2.9714285714285715})

Evals on base vs fine-tune

Base model:

Evaluation complete - Accuracy: 0.2400 (240/1000)

Performance by style: Ancient Egyptian architecture: 0.09 (5/57) Art Deco architecture: 0.23 (17/75) Art Nouveau architecture: 0.01 (1/73) Baroque architecture: 0.26 (15/58) Bauhaus architecture: 0.00 (0/58) Brutalism: 0.00 (0/38) Byzantine architecture: 0.34 (17/50) Chicago school architecture: 0.06 (3/51) Colonial architecture: 0.30 (27/89) Deconstructivism: 0.00 (0/38) Gothic architecture: 0.98 (59/60) Greek Revival architecture: 0.45 (26/58) International style: 0.00 (0/66) Neoclassicism: 0.14 (18/125) Postmodern architecture: 0.94 (47/50) Romanesque architecture: 0.09 (5/54) Base model results saved to paligemma448_arch_finetune_styles/base_model_folder_eval_20250316_183525.csv

=== EVALUATION RESULTS COMPARISON === Fine-tuned model accuracy: 0.8440 Base model accuracy: 0.2400 Improvement: 0.6040

The checkpoint-176 performs better than the latest checkpoint by .02, even though the training loss is lower on the latest checkpoint.

Framework versions

• Transformers 4.50.0.dev0
• Pytorch 2.6.0+cu124
• Datasets 3.4.0
• Tokenizers 0.21.0