metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_model_88
results: []
test_model_88
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the corranm/first_vote_100_per_new2 dataset. It achieves the following results on the evaluation set:
- Loss: 1.8934
- F1 Macro: 0.0606
- F1 Micro: 0.1591
- F1 Weighted: 0.0846
- Precision Macro: 0.0421
- Precision Micro: 0.1591
- Precision Weighted: 0.0586
- Recall Macro: 0.1132
- Recall Micro: 0.1591
- Recall Weighted: 0.1591
- Accuracy: 0.1591
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | F1 Weighted | Precision Macro | Precision Micro | Precision Weighted | Recall Macro | Recall Micro | Recall Weighted | Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.9682 | 0.8 | 3 | 1.9070 | 0.0599 | 0.2121 | 0.0848 | 0.0661 | 0.2121 | 0.0908 | 0.1486 | 0.2121 | 0.2121 | 0.2121 |
| 1.8993 | 1.8 | 6 | 1.8860 | 0.0902 | 0.2197 | 0.1243 | 0.0630 | 0.2197 | 0.0867 | 0.1594 | 0.2197 | 0.2197 | 0.2197 |
| 2.3539 | 2.8 | 9 | 1.8915 | 0.0637 | 0.1591 | 0.0887 | 0.0443 | 0.1591 | 0.0616 | 0.1141 | 0.1591 | 0.1591 | 0.1591 |
Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0