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---
library_name: transformers
license: gemma
base_model: google/paligemma2-3b-pt-448
tags:
- generated_from_trainer
model-index:
- name: paligemma-architecture-styles
results: []
language:
- en
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# paligemma-architecture-styles
This model is a fine-tuned version of [google/paligemma2-3b-pt-448](https://huggingface.co/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 |